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The Northeast Plain in China ranks among the top five regions that have been significantly impacted by haze pollution. To effectively control pollution, it is crucial to accurately assess the effects of emission reduction measures. In this study, we analyzed surveillance data and found substantial decreases (ranging from 19.0% to 50.1%) in average annual mass concentrations of key pollutants (such as CO, SO 2 , NO 2 , and PM 2.5) in the Northeast Plain from 2016 to 2020. To precisely determine the contributions of meteorological conditions and emission reductions to the improvement of air quality in the Northeast Plain, we conducted three scenario simulations. By comparing source emissions in December 2016 and 2020 using the WRF-Chem model (except for SO 2), we observed significant reductions of 21.3%, 8.8%, and 9.8% in mass concentrations of PM 2.5 , NO 2 , and CO, respectively, from 2016 to 2020. This highlights the essential role that meteorological conditions play in determining air quality in the Northeast Plain. Moreover, further reducing source emissions by 30% in December 2016 resulted in subsequent reductions of 25.3%, 29.0%, 4.5%, and 30.3% in mass concentrations of PM 2.5 , SO 2 , NO 2 , and CO, respectively, under the same meteorological conditions. Notably, source emission reduction was effective for PM 2.5 , SO 2 , and CO, but not for NO 2. The improvement in air quality in the Northeast Plain from 2016 to 2020 can be attributed to the combined effects of improved meteorological conditions and reduced pollution sources. [ABSTRACT FROM AUTHOR]

The Iberian Peninsula (IP), where this study is conducted, has experienced an increase of the frequency and intensity of Saharan aerosol dust outbreaks over the latest decades, which may have an impact on its regional climate. The Weather Research and Forecasting model coupled with chemistry (WRF-chem) has been used worldwide to simulate dust outbreaks and can support the analysis of such potential impacts. However, it includes multiple alternative aerosol parameterization choices that have not been conveniently evaluated in the study region yet. Here, three of the most popular WRF-chem aerosol parameterization schemes, namely, the Goddard Chemistry Aerosol Radiation and Transport (GOCART), the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) and the Modal Aerosol Dynamics Model for Europe (MADE) schemes, are inter-compared during a strong and dry dust outbreak on July 2021 in southern IP. The results show that the three schemes predict qualitatively similar dust intrusion patterns that are consistent with ground observations and have inter-model dust loading differences smaller than 4%. However, their average dust size distributions differ notably. While GOCART is reasonably consistent with observations, MOSAIC underpredicts the amount of dust particles with sub-micron diameters and overpredicts that of large particles and MADE does the opposite. This is found to have a strong detrimental impact on the prediction performance of dust optical properties in MOSAIC and MADE, which is related, at least partially, with issues in the required inter-sectional redistribution of dust parameters during the dust emission and calculation of optical properties. Overall, GOCART generally appears a better choice for strong and dry dust outbreak events in southern IP. It remains to be evaluated during wet dust outbreaks, which is a work underway. • WRF-chem aerosol schemes yield distinct results simulating a dry dust outbreak in southern Spain on July 2021. • Results with GOCART are more consistent with observations of dust optical properties and size distribution. • MOSAIC and MADE present issues related with the representation of mineral dust size distribution. [ABSTRACT FROM AUTHOR]

Valley topography is recognized for its role in constraining pollutant dispersion, which frequently results in elevated pollutant concentrations within valley regions. However, the specific mechanisms by which valley topography influences daytime ozone (O 3) production, nighttime O 3 depletion, and diurnal variations in O 3 concentrations remain inadequately understood. This study employs the online WRF-Chem air quality model to conduct sensitivity analyses, examining the effects of valley topography on summer O 3 concentrations in Lanzhou, a valley city in western China. The results indicate that valley topography generally reduces surface temperature and wind speed while also lowering the planetary boundary layer height (PBLH), which ultimately leads to increased concentrations of primary pollutants. Nevertheless, the impact of valley terrain on O 3 is time-dependent, with concentrations decreasing during the day and increasing at night. In Lanzhou, valley topography contributes to a 4.4% reduction in daytime O 3 concentrations. This reduction is largely due to the blocking of solar radiation by surrounding mountains, which results in a 7% decrease in shortwave radiation (SR) and a 17% decline in PBLH, subsequently limiting O 3 chemical production. Although valley topography restricts pollutant dispersion, the overall effect during the day is a net decrease in O 3 levels. In contrast, nighttime valley topography leads to a notable 19.8% increase in O 3 concentrations. This increase is driven by mountain breezes transporting O 3 -rich air from the slopes into urban areas, along with a 67.4% reduction in nitrogen oxide (NO) levels. Despite intensified NO titration within the valley, the combined effect of these local wind patterns contributes to elevated O 3 concentrations at night. These findings offer valuable insights into the unique factors contributing to urban O 3 pollution in areas with complex valley topography. [Display omitted] • Valley topography reduces solar radiation and limits daytime O 3 chemical production. • Mountain breezes raise nighttime O 3 by transporting O 3 -rich air into cities. • Complex topography affects O 3 differently than other primary pollutants. [ABSTRACT FROM AUTHOR]

Northeast Asia suffers from high concentrations of particulate matter (PM), prompting nations to actively implement emission reduction policies. This study evaluated the recent inter-annual changes in the chemical transformation and transboundary transport of PM over Northeast Asia, based on both ground and aircraft measurements, as well as WRF-Chem simulations, during two comprehensive campaigns: the Korea–United States Air Quality (KORUS-AQ) campaign in 2016 and the Satellite Integrated Joint Monitoring of Air Quality (SIJAQ) campaign in 2022, both conducted around the Korean Peninsula. Ground measurements in 2022 revealed significant reductions in air pollutants compared to 2016 levels. In the Beijing–Tianjin–Hebei region, PM 2.5 and SO 2 concentrations decreased by 47.2% and 73.9%, respectively, attributable to successful SO x and NO x emission reduction strategies. Similar trends were observed in downwind areas, including Seoul, where PM 2.5 and SO 2 levels declined by 30.0% and 41.4%, respectively. WRF-Chem model results indicated substantial decreases in sulfates, nitrates, and their precursors in both surface and upper atmosphere in 2022 compared to 2016. Moreover, model-calculated gas-to-particle conversion ratios, which peaked in the Yellow Sea in 2016, decreased by 15% in 2022 and shifted slightly eastward to the western Korean Peninsula. This shift suggests a potential decline in secondary PM formation processed in the Yellow Sea, coinciding with reduced long-range transport of gaseous pollutants. A comparison of model sensitivity experiments, accounting for both bottom-up emission changes and meteorological variations, revealed that while weather and climate factors such as precipitation and pressure patterns between 2016 and 2022 contributed to the overall decrease in PM concentrations, the primary driver was the reduction in emissions during this period. This study highlighted that the main driver of the substantial improvement in air quality over East Asia was the implementation of emission reduction policies targeting PM and its precursors in the main source regions in China. [Display omitted] • Between 2016 and 2022, PM 2.5 and SO 2 concentrations in the Beijing-Tianjin-Hebei region dropped by 47% and 74%, respectively. • PM 2.5 reduction in East China leads to a decrease in transboundary transport of air pollutants to downwind areas. • Emission reductions in upwind area also affect gas-to-particle conversion in the downwind areas. [ABSTRACT FROM AUTHOR]

Accurate modeling of aerosol-cloud interactions is essential for reliable weather and air quality simulations, given their significant impact on precipitation patterns, cloud dynamics, and aerosol distributions. This study employed the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to examine the impact of enhanced meteorological simulations, achieved through advanced microphysics parameterization supported by data assimilation techniques, on air quality across Texas on August 19 and 20, 2022. We tested four distinct configurations: (1) the Morrison two-moment bulk microphysics scheme, (2) Morrison's with observation nudging, (3) the Spectral Bin Microphysics (SBM), and (4) SBM with observation nudging. While the SBM scheme is known for its detailed representation of aerosol-cloud interactions, our focus was on how improvements in meteorological accuracy translate to more precise air quality simulations. Our findings demonstrated a progressive improvement in simulation accuracy, starting with the Morrison's scheme and further enhanced by adopting the SBM scheme, complemented by incorporating observation nudging. Specifically, the combination of the SBM scheme and the nudging substantially enhanced the model's ability to capture convective precipitation events, as shown by better alignment with NEXRAD radar reflectivity, with R increasing from −0.21 to 0.82, IOA from 0.10 to 0.87, and NMB decreasing from 99% to 34% in Houston. The enhanced meteorology translated into more accurate PM 2.5 concentration simulations, particularly through the more accurate representation of aerosol washout during precipitation events. In Houston, the SBM scheme with nudging improved the model's PM 2.5 simulations significantly, with NMB decreasing from −20% to 5% and IOA improving from 0.43 to 0.61. In San Antonio, improvements were also notable, with NMB improved from −27% to −22%, R increased from 0.48 to 0.82, and IOA increased from 0.66 to 0.86. Our results underscore the crucial role of accurate meteorological simulations in refining our understanding of aerosol behaviors in relation to precipitation patterns, directly enhancing the reliability and effectiveness of air quality modeling. • Demonstrates how SBM and observation nudging enhance WRF-Chem simulations of aerosol-cloud interactions over Texas. • Detailed microphysics scheme improves accuracy in simulating cloud dynamics and aerosol washout in urban settings. • Highlights the impact of real-time data integration on the accuracy of urban air quality and weather simulations. [ABSTRACT FROM AUTHOR]

In recent years, China has made certain progresses regarding the monitoring of ozone (O 3) pollution. However, a comprehensive understanding of the three-dimensional distribution and evolutionary characteristics of O 3 is still relatively lacking. This study analyzed an O 3 pollution episode in the Pearl River Delta during September to October 2019 using data from five O 3 lidars and air pollution monitoring stations combined with the Gridpoint Statistical Interpolation data assimilation system. The model's performance in the vertical direction improved after assimilation, with the correlation coefficient of model simulations improving by 87 %, errors decreasing by 30 %, and simulation accuracy increasing by 29 %. Results showed that O 3 pollution persisted for more than a week due to high temperatures and the influence of Typhoon Mita. However, after October 2, the concentration of O 3 decreased under the combined effects of southerly winds and reduced traffic emissions during the National Day period. Besides, lidar observations indicate that residual O 3 at night can disrupt its typical diurnal variation patterns. Moreover, O 3 pollution occurred both at ground level and greater heights. Overall, O 3 is primarily produced by photochemical reactions. Nevertheless, the O 3 concentration was also affected by pollutant transport from northeast to southwest at a height of 0–3 km during the pollution period, with an average transport flux of 366 μg·m−2·s−1. Anomalously high nighttime O 3 concentrations (>100 μg·m−3) were observed in both coastal and inland cities. Residual O 3 could lead to persistent pollution the next day. The results clarify the vertical distribution and evolutionary characteristics of O 3 , which is crucial for devising more targeted pollution control strategies in the Pearl River Delta. • Lidar observation network was used to observe ozone in the Pearl River Delta. • Three-dimensional data assimilation improved the simulation performance of ozone. • The regional transport flux was calculated using the assimilated results. • The reasons for abnormally high nighttime ozone concentration were analyzed. [ABSTRACT FROM AUTHOR]

National Capital Territory of Delhi and its satellite cities suffer from poor air quality during the post-monsoon months of October–November. In this study, a novel attempt is made to estimate the contribution of different emission sources (industrial, residential, power generation, transportation, biomass burning, photochemical production, lateral transport, etc.) towards the criteria air pollutant carbon monoxide (CO) concentration over North India. Multiple simulations of the WRF-Chem model with a tagged tracer approach with different inputs (6 anthropogenic emission inventories and 3 biomass burning emission inventories) were used. The model performance was evaluated against the MOPITT retrieved CO surface concentration. Analysis of model simulated CO over North India suggests that anthropogenic emissions contribute around 32–49% to surface CO concentration while crop residue burning contributes 27–44% of which 80% originates from Punjab. For Delhi, the contribution from anthropogenic sources is dominant (53–77%) of which 10–28% is from the domestic sector and 14–55% is from the transport sector. Agricultural waste burning contributes about 15–30% to Delhi's surface CO concentration (of which 75% originates from Punjab). Crop residue burning emission is a chief source of CO over Punjab with a contribution of about 56–76%. The results suggest that industrial, transport, and domestic sector activities are more responsible for increased CO levels over New Delhi and surrounding regions than crop residue burning over Punjab. Furthermore, critical meteorological parameters like 10 m wind speed, boundary layer height, 2 m temperature, total precipitation, and relative humidity were evaluated against CO concentration to understand their impact on CO distribution. Results conclude that deteriorating air quality over the North Indian region is caused by a combination of prevailing meteorological factors (such as slow winds, shallow mixing layer, and cold temperatures) and man-made emissions. [Display omitted] • Anthropogenic and biomass burning emissions contribute around 32–49% and 27–44% to total surface CO conc. over North India. • Around 53–77% Delhi CO is from anthropogenic activities. 15–30% CO is from crop residue burning. • Punjab CO concentration dominated by crop residue burning emissions (56–77%). • Transport and residential sector largest source for Delhi CO, meteorological conditions heavily impact pollutant transport. [ABSTRACT FROM AUTHOR]

Ground ozone (O 3) pollution has emerged as a prominent environmental concern in eastern cities of China, particularly during the summer and autumn seasons. However, a comprehensive investigation into the three-dimensional (3-D) evolution characteristics of O 3 within complicated urban environments, especially in lake-land environment, is notably scarce. To enhance our understanding of the mechanisms underlying elevated O 3 concentrations within a 3-D scale, this study employed an ozone lidar to delineate vertical ozone profiles in Changzhou, a typical city in China with complicated anthropogenic and biogenic emissions and complex land cover. The process analysis tool integrated into the Weather Research and Forecasting with Chemistry (WRF-Chem) model was further utilized to analyze the formation processes of O 3. The results unveil a persistent O 3 pollution episode lasting over 15 days in Changzhou during the study period, with multiple peaks exceeding 200 μg m⁻³. Notably, O 3 predominantly accumulated within the boundary layer, confined below 1.2 km. Both ground and vertical contributions to this pollution were mainly due to local chemical reactions, with a maximum near-surface contribution reaching 19 ppb h−1 and a vertical contribution of 10 ppb h−1 at the height of 900 ± 200 m. Furthermore, episodes of the enhanced O 3 concentrations on August 9 and August 26, 2021, were influenced by external advection process. Our study also found that local circulation plays an important role in the accumulation of surface O 3 during certain periods. There was a temperature difference between the surface of Lake Tai and the adjacent land, resulting in the formation of lake-land breezes that facilitate the transport of O 3 from the lake surface to the terrestrial environment during pollution events. Our study emphasizes the necessity of reducing local pollutant emissions and implementing joint emission controls as the primary strategies for mitigating O 3 pollution in Changzhou and the surrounding region. [Display omitted] • 3-D evolution of O 3 pollution os analyzed with Lidar and modeling techniques in Changzhou. • Processes contributing to O 3 at various altitudes are quantified. • Lake-land breeze significantly influences O 3 accumulation and transport. [ABSTRACT FROM AUTHOR]

The present study investigated the aerosol-radiation-cloud interaction of anthropogenic black carbon (BC) during a severe fog-haze event by utilizing WRF-Chem, multi-satellite and in-situ observations, reanalyses datasets, and HYSPLIT model. The WRF-Chem model adequately captured the regional distribution of aerosol, cloud, and meteorological variables over the study domain. The maximum BC surface concentration (≥7 μg m−3) was predominantly evident over densely populated central and lower Indo-Gangetic Plain (IGP) regions. Although smoke aerosols usually persisted within 4 km above the surface at daylight hours, they reached as high as 6 km during nighttime, specifically over the central IGP regions. The heavily influenced areas by BC aerosols expanded to almost the entire landmass and the continental outflow region upon doubling the anthropogenic emission. The geographical distribution of maximum perturbations in net shortwave radiation flux at the surface closely matched the spatial patterns of the highest BC concentration, indicating a crucial role of BC in directly preventing the incoming sunlight from reaching the surface. Consequently, the reduced solar radiation at the ground might result in substantial surface cooling (between −0.3 and −0.5 °C), eventually prohibiting surface evapotranspiration and diminishing the outgoing sensible and latent heat fluxes. Also, more amplified cooling in the polluted atmosphere can prevent further development of the planetary boundary layer (PBL), increasing particle entrapment near the surface via an 'aerosol-radiation-PBL' feedback loop. However, the most striking contrast between the two scenarios (typical and polluted) was the prolonged and intensified warming (0.5–0.8 °C) in the mid-troposphere, causing a remarkable drop in moisture (more than two to three times) and hence burn-off of liquid cloud droplets due to the semi-direct effect of increased BC aerosols. The intense mid-tropospheric heating could remarkably enhance the updraft velocity, supporting the vertical transport of the additional moisture to higher altitudes, forming more ice clouds at night. Therefore, the current study highlighted the urgency of mitigating anthropogenic BC emissions in highly polluted regions since increased emissions could considerably worsen severe fog-haze conditions and influence both liquid water and ice clouds, depending on the atmospheric conditions. • Maximum black carbon concentration is observed over the central and lower Indo-Gangetic Plain in the usual scenario. • Perturbation in surface fluxes due to near-surface cooling is more prominent in the polluted scenario. • Black carbon-induced prolonged and intense mid-tropospheric heating is more prominent than the near-surface cooling. • Stronger mid-tropospheric heating accelerates vertical moisture transport and promotes the formation of more ice clouds. [ABSTRACT FROM AUTHOR]

Satellite-based measurements have emerged as an effective method for the top-down estimates of anthropogenic CO 2 emissions. Changes in the column-averaged dry-air mole fractions of CO 2 (XCO 2) in the atmosphere reflect contributions from both human activities and natural processes, posing challenges in accurately extracting anthropogenic XCO 2 signals and quantifying urban CO 2 emissions. Here, we introduce a novel method based on spatial autocorrelation to directly identify anthropogenic XCO 2 signals from satellite measurements of Orbiting Carbon Observatory-2 (OCO-2). These signals serve as constraints for atmospheric transport model simulations, enabling the verification of emission inventory over urban areas. Utilizing 35 OCO-2 overpasses over the Yangtze River Delta urban agglomeration, we demonstrate the effectiveness of local Moran's I statistics in detecting localized anthropogenic XCO 2 enhancements. The results show an average XCO 2 increase of 1.36–4.41 ppm in proximity to major cities and areas with intensive industrial activity. A case study near Nanjing, based on eight overpasses, reveals XCO 2 enhancements with peaks ranging from 2.26 to 4.72 ppm. To establish the relationship between these XCO 2 enhancements and CO 2 emissions, we conducted WRF-Chem simulations driven by emissions from the Emissions Database for Global Atmospheric Research (EDGAR). Discrepancies between observed and simulated XCO 2 enhancements were primarily attributed to uncertainties in the prior emissions, the calculation of urban XCO 2 enhancements from OCO-2 data, and complex atmospheric transport dynamics. From our estimates, the daily CO 2 emissions in Nanjing is 0.65 ± 0.15 MtCO 2 /day, which is different from the EDGAR inventory by −10.5 % to 77.3 % (i.e., 0.17 ± 0.14 MtCO 2 /day). Error analysis suggests an uncertainty in CO 2 emission estimates associated with XCO 2 enhancement and wind speed ranging from 16 % to 32 % (i.e., 0.08–0.15 MtCO 2 /day). This study proposes an objective approach to assess urban CO 2 emissions, leveraging satellite XCO 2 observations to improve accuracy and reliability in emission inventories. • A novel approach is developed to identify anthropogenic XCO2 enhancement from OCO-2. • The Yangtze River Delta urban agglomeration show an XCO2 enhancement of 1–6 ppm. • he CO2 emissions in Nanjing from OCO-2 and the EDGAR differs by up to 77 %. [ABSTRACT FROM AUTHOR]

• Sea salt aerosols derives higher rainfall simulation performance than anthropogenic aerosols. • Aerosol transport from Europe leads to poor rainfall simulations. • Winter and autumn simulations are better than spring and summer simulations. Given the rapid spatiotemporal variability of aerosols and their complex impacts on the Earth system, investigating the role of aerosols in modelling extreme weather events, particularly various extreme rainfalls, remains limited. This study explored extreme rainfall simulation sensitivity to aerosol properties, transport and seasonality over the UK and Ireland during four seasons in 2020 by the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) model. Two sets of high-resolution simulations, one including aerosol direct and indirect effects and the other one without any aerosol effects, were conducted to investigate the improvements due to aerosol inputs. Meteorological results were verified using ground and satellite observations to examine the reliability of simulations. The 12 extreme rainfall events during the study months were classified by their backward trajectories from the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, and their simulation accuracies were quantified through six spatiotemporal metrics and one overall score. By comprehensive analyses of the simulation performances under different conditions, it is found that the model performs better in simulating rainfall events driven by sea salt aerosols (SSA) compared to those promoted by anthropogenic aerosols. Air masses transported from the Arctic and North Atlantic Ocean also help achieve higher rainfall simulation accuracy than European air masses. Rainfall simulations for winter and autumn events outperform those for spring and summer events. When considering aerosol effects in simulations, almost half of the events showed improved performance, while an equal number experienced a decrease and a small proportion remained unchanged. The most remarkable improvements are observed in rainfall simulations with higher concentrations of SSA and the participation of anthropogenic aerosols, while simulations promoted only by anthropogenic aerosols all experience performance reductions. The aerosol effects also led to significant increases in monthly rainfalls, accompanied by great reductions of Cl in larger particle size aerosols and NO 3 in smaller particle size aerosols. [ABSTRACT FROM AUTHOR]

The escalating levels of aerosols have emerged as a significant concern for climate extremes in the Southeast Asian regions, particularly accentuated during El Nino Years. In this study, we utilized a combination of atmospheric measurements and modeling to investigate the seasonal and geographical distribution of PM 2.5 mass concentrations and its primary contributors across Thailand during La Nina (2017) and El Nino (2019) years. Our findings revealed the highest daily mean surface PM 2.5 mass concentration during the summer season across most regions, followed by winter, and the lowest levels during the rainy season. Notably, a significant shift in daily mean PM 2.5 mass was observed from 2017 to 2019 across all seasons, with a pronounced increase of 70.9% over northern Thailand during the summer, attributed to heightened biomass burning (BB) contributions. In terms of source attribution for PM 2.5 mass, the primary contributors in 2017 were BB at 34.3%, followed by anthropogenic emissions (AE) at 32.4%, and natural emissions (NE) at 30.1%. However, in 2019, the BB contribution surged to 47.4% nationwide. During El Nino years, BB played a significant role in shaping concentrations during summer and winter, while NE dominated the rainy season. An important implication of our findings is the significant potential for reducing daily and annual mean PM 2.5 mass levels throughout Thailand by effectively reducing BB emissions. [Display omitted] • Aerosol increases in Southeast Asia, notably during El Niño Years. • Summer season shows the highest PM 2.5 levels in various regions, followed by winter. • PM 2.5 levels surged by 70.9% in northern Thailand during the summer (2017–2019). • In 2017, main PM 2.5 sources: biomass burning (34.3%) and human activities (32.4%). • Reducing biomass burning can significantly lower PM 2.5 levels across Thailand. [ABSTRACT FROM AUTHOR]

Liguria, located in the Northwest of Italy, is one of the Italian regions hit in the recent past by some of the most severe precipitation events among the ones observed in Italy. From a synoptic point of view, similar configurations characterize the extreme events that affected Liguria, i.e. the simultaneous presence of a deep pressure minimum west of the region and a strong high pressure over eastern Europe. Such conditions are favorable to the triggering of a quasi-stationary mesoscale V-shaped convective system over the Ligurian Sea. Furthermore, this kind of configuration is favorable to the formation and intrusion of wide plumes of aerosol, mainly mineral dust from the Sahara Desert and sea salt aerosols generated under high wind conditions in the Mediterranean basin. The present study aims at evaluating the impact that these aerosol plumes can have on meteorological fields during the triggering and evolution of the deep convective systems responsible for the Liguria flooding events. This study is carried out through numerical simulations performed with the WRF-Chem model, version 4.0. In particular, our main aim is to investigate the influence that the so-called direct (aerosol-radiation) and indirect (aerosol-cloud) interactions may have on mesoscale V-shape convective systems and on the associated rainfall events. In the simulations in which the direct aerosol effects are switched on, a consistent weakening signal is identified in the wind speed in all simulations; a major role seems to be played by the increase of downward longwave radiation in the presence of aerosol, that, by reducing the land-sea temperature gradient, causes the weakening of surface winds. More complex are the consequences of aerosol on the amount/intensity of the resulting precipitations. Unexpectedly, a reduced convergence is not necessarily associated with a weakening of precipitation and in half of the studied cases the interaction between aerosol, moisture and radiation increases the instability despite the reduced dynamical forcing due to the weakening of the convergence. [Display omitted] • Aerosol direct/indirect effects on deep convective systems are investigated via coupled chemistry-meteorology simulations. • The direct effects associated with the aerosol-radiation feedback appear to overcome the aerosol indirect effects. • Longwave radiation emitted by the aerosol layer attenuates land–sea thermal contrast. • Aerosol-radiation feedback is responsible for a significant attenuation of the winds. • Aerosol direct and indirect effects on precipitation associated on deep convective systems are not univocal. [ABSTRACT FROM AUTHOR]

Using the WRF model with chemistry (WRF-Chem), this study investigates the potential contributions of aerosol direct (ADE) and aerosol indirect (AIE) effects on the nocturnal convective precipitation occurred on 9–10 September 2019 in Beijing. It shows that ADE plays a more important role in spatiotemporal changes of heavy precipitation than AIE in urban areas. Further simulation analyses show that ADE suppresses precipitation by inducing greater surface shortwave radiative cooling and stability before 13:00 (UTC, hereafter) on 9 September, which reduces convective available potential energy (CAPE) losses and then increases the later CAPE. The ADE-induced CAPE increase makes updrafts stronger, increasing cloudiness and transporting more cloud water into the upper troposphere. This, in turn, causes greater latent heat release from freezing and then stronger convection, producing higher rain rate during 13:00–21:00. During 12:00–18:00, AIE causes a decrease in CAPE, which weakens updrafts. AIE decreases the snow mixing ratio above 700 hPa except at 15:00–16:00 on 9 September, which can decrease the release of latent heat from freezing, contributing to the decrease in precipitation after 18:00. Due to the greater stability before 10:00 and the longer time required for updrafts to overcome this stability, ADE necessitates 2–3 h to adjust the simulated rainfall time series. • ADE plays more important roles than AIE in change of heavy precipitation studied. • ADE suppresses daytime precipitation with peak rate delayed by 2 h. • ADE enhances while AIE slightly suppresses nighttime precipitation. [ABSTRACT FROM AUTHOR]

Dust aerosols pose significant modulations on precipitation, varying with precipitation intensity. In particular, the microphysical influences of dust aerosols on the clouds and diurnal variations of heavy precipitation exhibit substantial uncertainties. Taking the typical heavy sandstorm event occurring on March 15, 2021 in China as an example, we investigate the distinct modulations of dust aerosols on precipitation by the Weather Research and Forecasting model coupled to Chemistry (WRF-Chem) model and multiple observational and reanalysis data. Note that dust aerosols exert opposite influences on moderate and heavy precipitation. The inhibiting effect of dust aerosols on the intensity of light precipitation was observed as dust aerosols increase atmospheric stability by cooling in the lower layers, warming in the upper layers, reducing CAPE (Convective Available Potential Energy), increasing CIN (Convective Inhibition). Additionally, dust aerosols hinder the transformation of cloud droplets to raindrops, leading to decreased mass of rainwater (Qr) and snow (Qs) in the lower and middle atmosphere. In contrast, dust aerosols delay but intensify heavy rainfalls. During the night of heavy rain, dust aerosols can can warm the lower layers and cool the upper layers, increase CAPE and decrease CIN, thereby enhancing convection, making it easier for small cloud droplets to grow into larger raindrops through collision and coalescence. Simultaneously, small cloud droplets in the mid-levels ascend to higher altitudes where they freeze, transforming into ice particles. This process, coupled with the Bergeron-Findeisen mechanism, enhances the intensity of nighttime. These findings provide novel evidence to improve our understanding of dust aerosols-precipitation interaction. • Dust aerosols promote (suppress) heavy (light) rain by altering atmospheric stability and hydrometeor mass. • Dust aerosols enhance nighttime heavy rain by serving as ice nuclei and promoting convection and droplet growth. • Dust aerosols suppress precipitation by stabilizing the atmosphere and inhibiting droplet growth as CCN. [ABSTRACT FROM AUTHOR]

With fast economic development, urban expansion and air pollution are two main eco-environmental challenges confronting China. Though both are associated with intensified human activities, their impacts on urban environment are investigated separately, and the synthetic effect and intricate interaction of the two have not been fully understood. Here, based on in-situ measurements and online-coupled meteorology-chemistry modelling, the impacts of anthropogenic aerosol and urbanization are investigated simultaneously at Beijing-Tianjin-Hebei city cluster during a typical pollution season. The two processes exhibit opposite influences on meteorology and air quality. Aerosol via its radiative effect tends to cause net loss of solar energy with 3 °C cooling at the ground surface and 1 °C warming in the upper boundary layer. In contrast, urbanization yields large increment of net surface solar radiation as well as sensible heat flux, leading to over 1 °C warming in the lower atmosphere. Such modifications of boundary layer structure alter the accumulation and formation of air pollution. Urbanization effect dominated in the modification of near-surface air temperature and air pollution, making the synthetic effect a linear combination of two processes. The study highlights a comprehensive understanding of air pollution and urbanization and their synergy effect on urban environment over megacities. • The respective impacts of aerosol radiative effect and urbanization are revealed. • The synthetic effect of aerosol and urbanization exhibits a linear combination. • Aerosol was found to enhance urban heat island via altering stability/radiation. [ABSTRACT FROM AUTHOR]

The state of Qatar has been experiencing rapid urbanization with around 85% of its population residing in Doha. The country faces notable challenges related to the Urban Heat Island (UHI) effect, which is exacerbated by its hot and humid desert climate. This study focuses on analyzing the UHI phenomenon in Doha, utilizing observed meteorological data and the Weather Research and Forecasting model (WRF v4.5). Two land use and land cover (LULC) datasets from 2001 and 2018 are employed, with simulations conducted using different urban canopy models. The 2018 LULC data includes 100 m resolution information that categorizes urban areas into 11 local climate zones. Results indicate significant UHI intensity during both winter and summer periods (up to 6.5 °C), with notable differences observed between daytime and nighttime temperatures. In agreement with observations, the model predicts not only a strong UHI effect during nighttime, but also a significant Urban Cool Island effect in Doha (up to −5.8 °C during daytime in summer). The impact of various urban parameterization schemes on simulation accuracy is highlighted. The building energy model demonstrates superior performance in predicting temperature and relative humidity during the summer period. The spatial distribution of heat index illustrates the intensified warming in the city. • The region's first numerical study of UHI with a recent urban classification. • Introduction of Local Climatic Zones (LCZ)with the BEM urban parameterization improves predictions. • Significant UHI intensity during both winter and summer periods (up to 6.5 °C). • Simulations show extreme heat index values posing potential risks to human health. [ABSTRACT FROM AUTHOR]

In Latin America, the levels of pollution have risen considerably in the last few years. 2019, for example, had one of the largest numbers of air quality alerts. These alerts signal an increase in respiratory diseases among the population. For this reason, this paper designs a preventive early alert system for air quality. This system compares three machine learning models and validates, through statistical and categorical parameters (9), that a stochastic model, combined with a convolution bidirectional recurrent neural network (1D-BDLM), has an accuracy of ≈ 93 ± 4% when forecasting the risk for each population group in all the monitoring stations. Likewise, it is also able to capture high pollution events without producing false alarms (≈ 10 ± 5%). This model is utilized to design an alert protocol (24 h in advance) before a pollution event occurs. The protocol distinguishes the level of alert and the type of population at risk, focusing on two objectives: pollution mitigation and risk reduction for the population. To reduce pollutant concentrations, this paper proposes limiting vehicle traffic in the most polluted city zones or, if necessary, throughout the entire area. In relation to stationary sources, this article proposes the implementation of monitoring measures in order to identify the most polluting factories and restrict their operation during a specific period of time. In regards to population risk, the protocol aims to reduce exposure time by recommending the avoidance of outdoor activities (in specific zones) and the use of protective gear, taking into consideration relevant differences between population groups. [Display omitted] • An Early Alert System for PM 2.5 based on risk probability forecasts was designed. • An 1D-Bidirectional-LSTM network is the best model to simulate PM 2.5 behavior. • The protocol distinguishes the level of alert and the type of population at risk. • The system can be used in any country with air quality measurement stations. • People between 12 and 65 years are more impacted than infants, children and the elderly. [ABSTRACT FROM AUTHOR]

• Monsoon withdrawal and anti-cyclonic circulation to spur extreme pollution events. • PM2.5 particles rose to dangerous level in Delhi. • Erratic and extreme weather events linkage with climate change. • Historic crisis in landlocked mega city jeopardizing normal life. The world's top ranked mega city Delhi is known for deteriorated air quality. However, the analysis of air pollution data of 5 years (2014–2018) reveals that years 2016 and 2017, which were marked by an unusual delayed withdrawal of monsoon, witnessed an unprecedented extreme levels of toxic PM 2.5 particles (≤2.5 µm in diameter) touching a peak level of ∼ 760 µg/m3 (24 hr average), immediately after the monsoon retreat, surpassing WHO standards by ∼ 30 time and Indian national standards by ∼ 12 times, jeopardising lives of its citizens. However, the normal monsoon withdrawal years do not show such extreme levels of pollution. The high resolution WRF-Chem model along with meteorological data are used in this work to understand that how the delayed monsoon withdrawal and associated vagarious anti-cyclonic circulation resulted in trapping externally generated pollutants ceaselessly under colder conditions, leading to historic air quality crisis in landlocked mega city in these selected years. The sensitivity analysis confirmed that when WRF-chem model forced the climatology of normal monsoon year (2015) to simulate the pollution scenario of 2016 and 2017 for the above time period, the crisis subsided. Present findings suggest that such unusual monsoon patterns are on the hook to spur extreme pollution events in recent time. [Display omitted] [ABSTRACT FROM AUTHOR]

South Asian air pollutants exert substantial effect on the climate and environment change over the Tibetan Plateau (TP). This paper investigates the trans-boundary transport of PM 10 , PM 2.5 , and O 3 from South Asia to the TP via WRF-Chem sensitive analysis. It is found that PM 10 , PM 2.5 , and O 3 over South Asia are mainly sourced from anthropogenic sources. South Asian emissions contribute up to 54% of PM 10 (PM 2.5) in the TP in the pre-monsoon season. For O 3 , the highest contribution of 18.13% is in the monsoon season, while the lowest contribution of 8.67% is in the winter season. To evaluate the respective contributions of anthropogenic and biomass burning emissions in South Asia to PM 10 , PM 2.5 , and O 3 in the TP, anthropogenic and biomass burning emissions in South Asia are considered separately. The results show that the largest percentage contribution of PM 10 (PM 2.5) from South Asian anthropogenic emissions to PM 10 (PM 2.5) in the TP with value of 35.86% (37.77%) occurs in winter, while the lowest percentage contribution of 16.52% (16.77%) appears in the monsoon season. For O 3 , the largest contribution of 17.74% from South Asian anthropogenic emissions appears in the monsoon season, whereas the lowest contribution of 8.59% occurs in winter. Compared to the anthropogenic emissions, the biomass burning emissions in South Asia have a relative less contribution to PM 10 , PM 2.5 , and O 3 in the TP. The largest contribution of PM 10 (PM 2.5) from South Asian biomass burning emissions to PM 10 (PM 2.5) in the TP is 20.04% (18.37%) in the pre-monsoon season. Conversely, the lowest contribution of 0.58% (0.53) appears in the monsoon season. O 3 from South Asian biomass burning emissions contributes <1% to O 3 in the TP in each season, implying that the biomass burning emissions in South Asia have little effect on O 3 in the TP. This study is of great significance to the ecological environment protection for the TP. • Contributions of South Asian emissions to PM 10 , PM 2.5 , and O 3 in the TP are quantified. • Anthropogenic emissions in South Asia have a larger contribution to PM 10 , PM 2.5 , and O 3 in the TP. • O 3 from South Asian biomass burning emissions contributes <1% to O 3 in the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

Dry deposition is an important ozone sink in the planetary boundary layer (PBL), but how different PBL parameterization schemes affect the ozone dry deposition impacts has not been well quantified. Here we investigate the influences of PBL mixing parameterizations on surface ozone and dry deposition in eastern China using a regional air quality model (WRF-Chem) and further quantify the contributions of dry deposition to ozone tendencies both near the surface and in the PBL with integrated process rates (IPR) analysis. We analyze three PBL schemes coupled with their corresponding surface layer (SFL) schemes, including Yonsei University (YSU), Mellor–Yamada–Janjić (MYJ), and Asymmetric Convective Model v2 (ACM2). We find that using different PBL-SFL schemes in general produces similar monthly mean meteorological fields, leading to relatively small uncertainties, ranging from 6.5% to 18.5% in monthly mean surface ozone concentrations and 3.6% to 15.3% in total ozone dry deposition in eastern China. However, these uncertainties can frequently exceed 30% in summer at the daily scale. IPR analyses with YSU_MM5 show that ozone dry deposition accounts for 80–89% of surface ozone losses and 45–58% of PBL ozone losses. Model sensitivity simulations by suppressing ozone dry deposition would enhance surface ozone in eastern China by 24–30% (20.6–35.7 μg m−3) during the daytime and 61–82% (26.0–46.1 μg m−3) at night. Ozone dry deposition significantly promotes the downward vertical mixing of ozone to the surface layer, which contributes to smoothing the near-surface ozone gradients as caused by ozone dry deposition and titration, thus explaining the smaller contribution estimated by the sensitivity approach than IPR analyses. We further show that different meteorological conditions simulated by different PBL-SFL schemes affect daily ozone simulations not only through the chemistry process as previously recognized but also considerably by the dry deposition process. [Display omitted] • The influences of PBL schemes on surface ozone and dry deposition in eastern China are investigated. • Different PBL schemes cause uncertainties of 3.6–15.3% in WRF-Chem simulated ozone dry deposition. • IPR analyses with YSU_MM5 show that ozone dry deposition accounts for 80–89% (45–58%) of surface (PBL) ozone losses. • Process analyses show dry deposition significantly promotes downward vertical mixing compensating for the surface ozone loss. [ABSTRACT FROM AUTHOR]

Biomass burning aerosols (BBAs) influence the climate directly by scattering and absorbing sunlight and indirectly by changing the cloud properties through serving as cloud condensation nuclei (CCN) or ice-nucleating particles (INPs). Here we used the WRF-Chem model to simulate the transport of BBAs and their interactions with clouds and radiation during the western United States wildfires in September 2020. The simulated cloud and aerosol fields are comparable to the satellite observations from CALIPSO and MODIS. It is shown that smoke exerts negative radiative effects over both ocean and land. Over the nearly cloudless land, the negative radiative effect is caused by aerosols reflecting shortwave radiation. However, over the ocean covered by stratocumulus, aerosol-cloud interaction (ACI) induces a significant negative radiative effect (−25.6 W/m2 on average), because the reduction of droplet radius not only increases the brightness of clouds and prolongs their lifespan, but also cools their surroundings and increases relative humidity, which in turn increases cloud water content. On the contrary, aerosol-radiation interaction (ARI) leads to a positive radiative effect (+10.3 W/m2 on average) over the oceanic regions due to the absorption of sunlight reflected by low clouds and a slight decrease in cloud water. In our research, the counterclockwise vortex over the Northeast Pacific transports the smoke to remote regions and lifts the smoke to 4–8 km height. This results in a wide-ranging impact of the smoke on clouds and radiation, yet it diminishes the magnitude of ARI's effect on marine stratocumulus. Moreover, over ocean, ARI results in cooling at the cloud top and warming close to the sea surface, leading to a rise in marine stratocumulus height. These vertical atmospheric adjustments within the PBL differ from the reduced surface temperature with suppressed PBL over terrestrial regions. • Aerosol-cloud interaction (ACI) and aerosol-radiation interaction (ARI) have opposite radiative effects over ocean. • ACI reduces cloud droplet size and increases cloud water, leading to a strong negative radiative effect over ocean. • ARI has minimal effects on total cloud water of marine stratocumulus, and has distinct effects over ocean and over land. [ABSTRACT FROM AUTHOR]

New particle formation (NPF) is a key process occurring in the planetary boundary layer (PBL). Newly formed particles are an important source of aerosols and cloud condensation nuclei (CCN) that influence clouds and climate. However, the distribution of these new particles at different altitudes has rarely been studied. In-situ measurements of ultrafine particles (UFP) observed at the ground level and at the top of the Canton Tower (454 m) located in downtown Guangzhou in southern China were analyzed, along with measurements of multiple meteorological and physicochemical quantities observed during a field campaign carried out from December 2019 to March 2020 and simulated with the Weather Research and Forecasting (WRF-Chem) model. We found that turbulence and NPF characteristics vary considerably with height, with UFP concentrations diminishing by half from the surface to the tower top. This indicates that UFP are transported upward from the ground in the lower boundary layer. A consistent relationship is established between the occurrences of NPF and the evolution of turbulence. The correlation between the exchange ratio at the tower top correlates well with nucleation growth, suggesting that turbulence can play an important role in episodes of NPF growth. The growth rate is closely related to the turbulence exchange ratio, effectively dictating the UFP concentration before and during the COVID-19 lockdown period. A new mechanism is thus hypothesized: NPF happens earlier near the surface and grows faster in the upper PBL, attributed to condensable vapor being transported by turbulent vertical mixing in the boundary layer. Model simulations using the WRF-Chem model reveal that the exchange ratio changed NPF parameters, supporting the proposed mechanism that the evolution of the PBL has a significant impact on NPF. This should not be neglected in NPF research because this physical factor may be the dominant one underlying the NPF mechanism. • New particle formation (NPF) observed at the ground and the top of the Canton Tower (454 m) found the feature vary considerably at different heights. • Turbulence evolution is closely related to the growth rate. • NPF happens earlier near the surface and grows faster at the upper PBL attributed to turbulent mixing. [ABSTRACT FROM AUTHOR]

Intensive crop residue burning (CRB) in northern India triggers severe air pollution episodes over the Indo-Gangetic Plain (IGP) each year during October and November. We have quantified the contribution of hotspot districts (HSDs) and total CRB to poor air quality over the IGP. Initially, we investigated the spatiotemporal distribution of CRB fire within the domain and pinpointed five HSD in each Punjab and Haryana. Furthermore, we have simulated air quality and quantified the impact of CRB using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), incorporating recent anthropogenic emissions (EDGAR v5) and biomass burning emissions (FINN v2.4) inventories, along with MOZART-MOSAIC chemistry. The key finding is that HSDs contributed ∼80% and ∼50% of the total fire counts in Haryana and Punjab, respectively. The model effectively captured observed PM₂.₅ concentrations, with a normalized mean bias (NMB) below 0.2 and R-squared (R2) exceeding 0.65 at the majority of validation sites. However, some discrepancies were observed at a few sites in Delhi, Punjab, Haryana, and West Bengal. The National Capital Region experienced the highest PM₂.₅ concentrations, followed by Punjab, Haryana, Uttar Pradesh, Bihar, and West Bengal. Moreover, HSDs were responsible for about 70% of the total increase in CRB-induced PM₂.₅ in the western, central, and eastern cities, and around 50% in the northern cities. By eliminating CRB emissions across the domain, we could potentially save approximately 18,000 lives annually. Policymakers, scientists, and institutions can leverage the framework to address air pollution at national and global scales by targeting source-specific hotspots. This approach, coupled with appropriate technological and financial solutions, can contribute to achieving climate change and sustainable development goals. [Display omitted] • Hotspots of Haryana and Punjab contributed ∼80% and ∼50% of the state total fire count respectively • WRF-Chem model simulated PM 2.5 peak (∼540 μg/m³) without nudging • Hotspot districts contributed ∼50–70% to total crop residue burning-induced PM 2.5 enhancement • ∼18,000 lives could be saved by eliminating crop residue burning [ABSTRACT FROM AUTHOR]

Urban areas contribute approximately 70% of the total anthropogenic CO 2 emissions, making them the primary focus of global carbon monitoring. However, accurate modelling of urban meteorology is challenging because of complex artificial landscapes. Reducing errors in urban meteorological simulation and improving the accuracy of modelling the spatial distribution of urban CO 2 is of great significance for the "top-down" CO 2 emissions estimation in urban areas. In this study, local urban datasets were constructed based on multiple data sources to overcome the limitation of insufficient information from a single data source. In the development of urban datasets, grid-by-grid data processing is realized, and the evaluation results show that the developed urban datasets are greatly improved compared with the default. The developed urban datasets were applied to WRF-Chem with 1-km spatial resolution, and the simulated column-averaged dry air mole fraction of CO 2 (XCO 2) was verified with the EM27/SUN observed data considering the slant observation path. The results show that urban datasets strongly influence the spatial distribution modelling of XCO 2 , depending on the state of the atmosphere near the surface, especially wind velocity. Accurate local urban datasets can effectively reduce the bias in urban XCO 2 spatial distribution modelling and improve the capability of the atmospheric CO 2 transport model in urban carbon emission estimation. • "Top-down" urban CO2 emissions estimation requires accurate urban XCO 2 spatial distribution modelling. • Local urban datasets were constructed using multi-source data to improve the CO 2 simulation. • The simulated column-averaged dry air mole fraction of CO 2 (XCO 2) was verified with the EM27/SUN observed data considering the slant observation path. • The 3D urban morphology cannot be ignored for urban carbon emission estimation, especially for precise matching of the model and measurement. [ABSTRACT FROM AUTHOR]

Accurate prediction of PM 2.5 , its optical properties and dominant chemical components are essential for air quality studies. In this study, we investigated the effects of two gas phase chemical schemes coupled with three aerosol mechanisms on the simulated PM 2.5 mass concentration in Delhi using the Weather Research and Forecasting model with Chemistry module (WRF-Chem). The model was employed to cover the entire northern region of India at 10 km horizontal spacing and results were compared with comprehensive field data set on dominant PM 2.5 chemical compounds from the Winter Fog EXperiment (WiFEX) at the Indira-Gandhi International Airport, New Delhi, and surface PM 2.5 observations in Delhi (17 sites), Punjab (3 sites), Haryana (4 sites), Uttar Pradesh (7 sites) and Rajasthan (17 sites). The Model for Ozone and related Chemical Tracers (MOZART) gas-phase chemical mechanism coupled with the Goddard Chemistry Aerosol Radiation and Transport (GOCART) aerosol scheme were selected in the first experiment as it is currently employed in the operational air quality forecasting system of Ministry of Earth Sciences (MoES), Government of India. Other two simulations were performed with the MOZART gas phase chemical mechanism coupled with the Model for Simulating Aerosol Interactions and Chemistry (MOZART-MOSAIC), and Carbon Bond 5 (CB-05) gas mechanism coupled with the Modal Aerosol Dynamics Model for Europe/Secondary Organic Aerosol Model (CB05 - MADE/SORGAM) aerosol mechanisms. The evaluation demonstrated that chemical mechanisms affect the evolution of gas-phase precursors and aerosol processes which in turn affect the optical depth and overall performance of the model for PM 2.5. All the three chemical schemes underestimate the observed concentrations of major aerosol composition and precursor gases over Delhi. Comparison with observations suggests that, the simulations using MOZART gas-phase chemical mechanism with MOSAIC aerosol scheme performed better in simulating aerosols over Delhi and its optical depth over the IGP. • PM 2.5 prediction differ for different gas phase and aerosol chemistry schemes in WRF-Chem. • Evaluated wintertime PM 2.5 prediction from MOZART-GOCART, MOZART-MOSAIC and CB05-MADE/SORGAM chemical mechanisms. • MOZART-MOSAIC schemes showed comparatively better performance in simulating aerosols over Delhi. • PM 2.5 chemical species are found to be underestimated by all gas-aerosol schemes used in this study over Delhi. [ABSTRACT FROM AUTHOR]

Ambient fine particulate matter (PM 2.5) is strongly associated with various adverse health outcomes. However, the lack of extensive PM 2.5 measurements, and especially of its components, hinders the assessment of negative health effects caused by PM 2.5 in many parts of the world. To address this issue, a new satellite instrument, the Multi-angle Imager for Aerosols (MAIA), with improved design for providing aerosol optical depth (AOD) of high quality, will be helpful in determining concentrations of total and speciated PM 2.5. According to the retrieval algorithm of MAIA particulate matter (PM) products, level 2 (L2) PM products are generated based on MAIA AOD on days of observation. Bias-corrected chemical transport model (CTM) outputs are then merged with the L2 PM products to fill their gaps using a Bayesian Model Averaging (BMA) ensemble framework. This process creates the MAIA Level 4 (L4) gap-filled PM products. In this study, we aim to implement the MAIA framework and validate its feasibility after the launch of the MAIA satellite instrument. We used both Bayesian hierarchical model (BHM) and a Bayesian additive regression tree (BART) to predict L2 and CTM-based daily 1 × 1 km2 PM 2.5 mass and speciation concentrations, along with prediction uncertainties, over the MAIA Primary Target Area in the Northeastern US in 2018. We then employed the BMA ensemble model to combine the L2 and CTM-based PM 2.5 mass predictions to fill gaps in L2 PM 2.5 mass and produce Level 4 (L4) gap-filled PM 2.5 mass. Our cross-validation experiments showed that both the BHM (R2 ranging from 0.60 to 0.82) and BART (R2 ranging from 0.59 to 0.79) models performed well in predicting CTM-based PM 2.5 speciation, with better results for sulfate, organic carbon, and elemental carbon. At the stage of L4 PM 2.5 mass predictions, both BHM-based and BART-based BMA ensemble models demonstrated improved performance with their traditional R2 of 0.81 and 0.73, surpassing the input L2 and CTM-based PM 2.5 mass. Additionally, our models showed excellent prediction uncertainty control with the coverage rates of 95% posterior prediction confidence interval associated of concentration estimates to be 95% for BHM and 75% for BART across PM 2.5 mass and speciation. Results from the proposed modeling techniques contribute to a deeper understanding of the health effects of PM 2.5 for future epidemiological studies and provide insights into the MAIA mission for producing improved PM products for health research. • Simulating MAIA framework in producing PM 2.5 products with quantified uncertainty. • BHM models are well-suited for modeling PM 2.5 speciation in data-poor regions. • BMA ensemble model fills gaps, improves L2 and CTM-based PM 2.5 estimates. • Providing pre-launch insights into MAIA framework and PM 2.5 products. [ABSTRACT FROM AUTHOR]

Dust storms negatively impact human health, and also lead to vehicle crashes due to reduced visibility. Improving our ability to model and predict the location, timing, and intensity of dust storms would therefore benefit society. The amount of dust emissions is controlled by soil composition, vegetation fraction, and other faster-varying factors like wind speed and soil moisture content. Greater soil moisture content increases the cohesive forces of soil particles, which in turn makes it more difficult for a given wind speed to loft dust particles in sufficient quantities to create a dust storm. Thus, improving the soil moisture representation in numerical weather prediction fully coupled with atmospheric chemistry models like WRF-Chem is hypothesized to be an important factor to improving predictions of dust aerosol content in the atmosphere. To test this hypothesis, we identified eight cases of dust storms over the western U.S. from 2015–2021. For each case we ran two seven-day WRF-Chem simulations: in one simulation we directly inserted soil moisture content retrievals from the Soil Moisture Active Passive (SMAP) satellite into WRF-Chem ("Insert SMAP"), while the other simulation had no soil moisture adjustments ("No SMAP"). The soil moisture representation in WRF was generally improved in many locations in the southwestern U.S. by directly inserting SMAP retrievals. Additionally, the Insert SMAP simulations consistently had higher values of aerosol optical depth (AOD) due to increased atmospheric dust loading; in cases where WRF-Chem under-predicted dust loading, the AOD simulations were improved in these regions, but in the cases where WRF-Chem already over-predicted dust loading, the additional dust in Insert SMAP produced slightly worse simulations of AOD. Overall, adjusting the soil moisture representation in WRF-Chem using SMAP data had a relatively small impact on AOD during these dust storms, which points to other lingering, larger sources of model error, such as the erodibility input dataset or dust emission parameterization in WRF-Chem, that improved soil moisture alone cannot resolve. • Demonstrates assimilation of soil moisture retrievals from the SMAP satellite into the WRF-Chem model via direct insertion. • Evaluated WRF-Chem simulations with/without SMAP soil moisture insertion for eight western U.S. dust storms from 2015–2021. • Assimilating SMAP soil moisture generally resulted in somewhat more dust being emitted in WRF-Chem during these dust storms. • Other model error sources (dust emission scheme, erodibility dataset) appear to dominate impacts of soil moisture on dust. [ABSTRACT FROM AUTHOR]

The vertical distribution and transport properties of dust aerosols play a key role in regional and even global climate and environmental changes. However, there are still gaps in understanding the optical properties, transport mechanisms, and topographic effects on dust in the dust-prone area-southern margin of the Tarim Basin. In this study, we use the Ground-Based Aerosol Lidar (GBQL-01) installed in Hotan and Minfeng in cooperation with the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) to synthesize and analyze the dust formation process at the southern margin of the basin once, and to test the reliability of the ground-based aerosol lidar. The variability characteristics of meteorological elements during this dust process will be discussed. Furthermore, the Weather Research and Forecasting Model with Chemistry (WRF-Chem) was used to reproduce this dust process. The results show that the vertical transport of dust particles reaches about 4 km and 3 km in Hotan and Minfeng, respectively, and the dust aerosol stratification intensity is obvious within 1 km height in both places, and there is a dust aerosol layer at 3 km height in both places. The high winds are blocked by the large topography of the Tianshan Mountains and the northern slopes of the Tibet Plateau, and they change to northeasterly winds, triggering dusty weather outbreaks at the southern margin of the Tarim Basin. When the dusty weather subsided, dust aerosols with concentrations of 300–400 μg m−3 were still present at an altitude of 4 km, and a clear suspended dust phenomenon was formed. Meanwhile, the increase of wind speed and relative humidity, as well as the decrease of temperature, easily contribute to the occurrence of dusty weather and the transport of particulate matter. This work provides a useful reference for the study of regional climate change, topographic effects and "suspended dust". • The evaluation of the ground-based aerosol lidar's performance was conducted in an integrated manner using CALIPSO and particulate matter data. This assessment is essential for future studies. • Data on the height of dust stratification in the southern margin of the Tarim Basin (Hotan and Minfeng) was collected, confirming the presence of a stable aerosol layer at an altitude of 3 km. • The analysis using the WRF-Chem model clearly demonstrates the influence of topographic effects on dust aerosols. Additionally, this study confirms the occurrence of a unique phenomenon known as "suspended dust.". [ABSTRACT FROM AUTHOR]

The 26th United Nations Climate Change Conference (COP26) proposes to limit global warming to <1.5 °C above pre-industrial levels until 2030 and aligns CO 2 emissions with net-zero by 2050. To achieve this goal, it is crucial to replace fossil fuels with renewable and clean energy sources. Among the various renewable energy sources, solar energy undoubtedly stands out as an attractive option. Future meteorological conditions and emission reductions are expected to impact on solar energy potential. Consequently, the Weather Research and Forecasting model with chemistry (WRF-Chem) was applied to current (2016–2020) and future (2046–2050) under the shared socio-economic pathways (SSP) 2–4.5 scenario to investigate the impact of future meteorological conditions and emission reductions on solar energy potential. The evaluation of the WRF-Chem demonstrates satisfactory performance in capturing most meteorological and chemical variables at a climatological scale. However, the model underestimates 2 m temperature while overestimating 2 m specific humidity and 10 m wind speed, with mean bias (MB) of −0.1 °C, 1.4 g kg−1, and 0.8 m s−1, respectively. Additionally, the WRF-Chem overestimates PM 2.5 , with normalized mean bias (NMB) of −20%. The model underestimates cloud fraction and precipitation caused by the limitation in the cloud microphysical parameterization. The model well reproduced the solar energy distribution in China, with R of 0.76 and NMB of −3%. Looking ahead, the future annual average solar energy increases by 2.2, 0.1, 2, 4.1, 1.9, and 2.9 W m−2 for China, Beijing-Tianjin-Hebei, Fenwei Plain, Yangtze River Delta, Pearl River Delta, and Sichuan Basin, respectively. The future annual average photovoltaic potential increase by 1–4% in these regions. This increase is primarily attributed to the increase in solar energy resulting from emission reductions (4.7, 5.1, 5.0, 4.7, 3.2, and 6.1 W m−2), which outweighs the decrease caused by meteorological conditions (2.5, 5.0, 2, 4.1, 1.9, and 2.9 W m−2). Hence, emission reduction plays a vital role in promoting solar energy utilization. • The future (2046–2050) solar energy changes compared to current (2016–2020) were assessed using WRF-Chem. • The WRF-Chem well reproduces most meteorological and chemical variables on a climatological scale. • The future annual average solar energy potential increases in China. • Pollution control policies must be taken into account when planning future solar energy resources. [ABSTRACT FROM AUTHOR]

Present study examines how ozone concentration changed under heatwave (HW) condition with emphasis on meteorological parameters in respect to non-heatwave (NHW) days. In this perspective, Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) has been used to simulate the surface O 3 (SfO 3) and maximum temperature (T max) during NHW (11th-19th May 2015) and HW days (21st-29th May 2015) over southeast (SE), India. The WRF-Chem simulated meteorological and chemical variables have been evaluated against the ERA5 and CAMS reanalysis dataset. A significant correlation of 55–95% is found for all the meteorological and chemical variables. The influencing parameters shows positive correlation of ozone with temperature, which reaches 75–78 ppbv under HW condition. Day to day trend analysis reveal an increasing pattern of maximum temperature and SfO 3 concentration under HW condition. During HW, mixing of ozone-rich air aloft with near-surface air leading a rise in SfO 3 , as indicated by both ERA5 (with a maximum Planetary Boundary Layer Height (PBLH) of 1000 m) and WRF-Chem simulations (1600 m). Furthermore, the diurnal cycle of SfO 3 , temperature, PBLH reaches a peak at afternoon, while the other variables like nitrogen oxides (NO x), Relative Humidity (RH) shows a high concentration at night-time. Overall, WRF-Chem model effectively captures the diurnal fluctuations of SfO 3 , NO x and the meteorological variables during the HW event over the SE, India. Result shows that HW may cause a strong contribution to the rate of increase in SfO 3 (22.17%). Thus, it is required to consider contribution of HW driven ozone when developing long-term strategies to mitigate regional ozone pollution. [Display omitted] • WRF-Chem examines ozone changes during heatwave condition in Southeast, India. • Robust statistics reveal a good agreement of WRF-Chem model with CAMS dataset. • Maximum Temperature and surface O 3 show an increasing shift during heat wave days. • Study has implications for improving numerical predictions of rising surface ozone. [ABSTRACT FROM AUTHOR]

This study investigates the influence of PM 10 from remote sources on PM 10 episodes over the city of Cape Town. For the study, we analysed observation data from Cape Town's air quality monitoring stations as well as high-resolution simulation data from the Weather Research and Forecasting model with Chemistry (WRF-Chem). The observation data were used to identify PM 10 episodes during the study period (2008–2014) and WRF-Chem was applied to simulate the atmospheric conditions and PM 10 transport over southern Africa during each episode. The capability of WRF-Chem to simulate the wind and PM 10 concentration over Cape Town was quantified and the paths of air parcels over Cape Town during each episode were tracked. Results of the study show that WRF-Chem gives a realistic simulation of observed wind (speed and direction) over the city during the episodes, but the model struggles to reproduce the observed PM 10 concentration. For most episodes, the magnitude of the simulated PM 10 is lower than the observed due to lack of local emissions in the simulations. In some cases, the model reproduces the peak in PM 10 concentration some days earlier or later than observed. The simulations show that most air parcels over Cape Town during the episodes have travelled over major dust source regions (i.e., the Kalahari or Namib Desert) before reaching the city. Most of episodes are associated with a southward transport of a plume of PM 10 from the north-west coast of southern Africa to Cape Town. This PM 10 plume is induced by a coastal trough and a continental high pressure system. In some cases, local topography influences the intrusion of the PM 10 plume into Cape Town by blocking some of the pollution, thereby minimising the amount of PM 10 that reaches the city. Results of the study suggest that the transport of PM 10 from the north-west coast of southern Africa may contribute to PM 10 episodes in Cape Town. • We investigate the transport of PM10 from remote sources into Cape Town during high pollution episodes over the city. • Most of the PM10 episodes in Cape Town are associated with a southward transport of PM10 from the Namib and Kalahari Deserts. • The transport of PM10 is induced by a trough along the west coast and a high pressure system over the continent. • The intrusion of the PM10 plume into Cape Town is minimized by topography around the city. [ABSTRACT FROM AUTHOR]

A three-dimensional variational (3DVAR) lidar data assimilation method is developed based on the Community Radiative Transfer Model (CRTM) and Weather Research and Forecasting model coupled to Chemistry (WRF-Chem) model. A 3DVAR data assimilation (DA) system using lidar extinction coefficient observation data is established, and variables from the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) mechanism of the WRF-Chem model are employed. Hourly lidar extinction coefficient data from 12:00 to 18:00 UTC on March 13, 2018 at four stations in Beijing are assimilated into the initial field of the WRF-Chem model; subsequently, a 24 h PM 2.5 concentration forecast is made. Results indicate that assimilating lidar data can effectively improve the subsequent forecast. PM 2.5 forecasts without using lidar DA are remarkably underestimated, particularly during heavy haze periods; in contrast, forecasts of PM 2.5 concentrations with lidar DA are closer to observations, the model low bias is evidently reduced, and the vertical distribution of the PM 2.5 concentration in Beijing is distinctly improved from the surface to 1200 m. Of the five aerosol species, improvements of NO 3 − are the most significant. The correlation coefficient between PM 2.5 concentration forecasts with lidar DA and observations at 12 stations in Beijing is increased by 0.45, and the corresponding average RMSE is decreased by 25 μg·m−3, which respectively compared to those without DA. Unlabelled Image • The 3DVAR assimilation system of lidar extinction coefficient data developed based on CRTM and WRF-Chem; • PM 2.5 forecasts with lidar DA close to observations, while those without DA remarkably underestimated; • The vertical distribution of PM 2.5 distinctly improved with lidar DA and significant improvement for nitrate. [ABSTRACT FROM AUTHOR]

We investigate the distribution of volatile organic compounds (VOCs) over Indian subcontinent during a winter month of January 2011 combining the regional model WRF-Chem (Weather Research and Forecasting model coupled with Chemistry) with ground- and space-based observations and chemical reanalysis. WRF-Chem simulated VOCs are found to be comparable with ground-based observations over contrasting environments of the Indian subcontinent. WRF-Chem results reveal the elevated levels of VOCs (e. g. propane) over the Indo-Gangetic Plain (16 ppbv), followed by the Northeast region (9.1 ppbv) in comparison with other parts of the Indian subcontinent (1.3–8.2 ppbv). Higher relative abundances of propane (27–31%) and ethane (13–17%) are simulated across the Indian subcontinent. WRF-Chem simulated formaldehyde and glyoxal show the western coast, Eastern India and the Indo-Gangetic Plain as the regional hotspots, in a qualitative agreement with the MACC (Monitoring Atmospheric Composition and Climate) reanalysis and satellite-based observations. Lower values of R GF (ratio of glyoxal to formaldehyde <0.04) suggest dominant influences of the anthropogenic emissions on the distribution of VOCs over Indian subcontinent, except the northeastern region where higher R GF (∼0.06) indicates the role of biogenic emissions, in addition to anthropogenic emissions. Analysis of HCHO/NO 2 ratio shows a NO x -limited ozone production over India, with a NO x -to-VOC transition regime over central India and IGP. The study highlights a need to initiate in situ observations of VOCs over regional hotspots (Northeast, Central India, and the western coast) based on WRF-Chem results, where different satellite-based observations differ significantly. Image 1 • First high-resolution simulation of VOCs over South Asia during winter. • Extensive model evaluation against in situ, satellite data, and MACC reanalysis. • Regional hotspots of enhanced VOC loadings are revealed. • Significant differences in formaldehyde observed from various satellites. [ABSTRACT FROM AUTHOR]

Abstract Frequent dust storms have harm to human health and agricultural activities in Central Asia. However, there has been a great deal of uncertainty in prediction of dust storms in Central Asia. One of the important reasons is that the adaptability of different dust emission schemes has not been evaluated. Here, the Goddard Chemistry Aerosol Radiation and Transport (GOCART), Air Force Weather Agency (AFWA) and Shao2004 (Shao04) dust schemes coupled to the WRF-Chem model were used to simulate the severe dust storm occurred in Central Asia on 12–15 July 2016. Generally, this dust storm was initialed by a vortex at 500 hPa and surface cold front, and then swept across Turkmenistan, Uzbekistan and Tajikistan. The Shao04 case could represent the spatial-temporal evolution of the dust storm well, especially at the northern Iran and Turkmenistan, due to its better description of the physical process of dust emission. But it overestimated the aerosol optical depth (AOD) to the southeast of the Aral Sea, which might be associated with the uncertainties of the soil particle distribution dataset. The AFWA case simulated AOD as better as Shao04 case with improved soil moisture correction factors, saltation algorithms and particle size distributions although it had smaller particle size, indicating that smaller particles are assignable. The GOCART case showed the largest dust emission areas due to the low threshold velocity. Yet both the AFWA and GOCART scheme underestimated the high AOD over northern Iran owe to its low erodibility factors. The total dust emission of the four-day period in the Shao04 scheme was 11.9 Tg, which was 2–3 times larger than those obtained in the AFWA and GOCART schemes. The significant differences of dust emission between three dust schemes may essentially depend on the sensitivities of threshold friction velocity on surface property. Highlights • AFWA and Shao04 schemes have better performance on dust storms modeling. • GOCART case only simulated high AOD over Turkmenistan. • Differences of dust emission in three schemes highly depend on parameters of scheme. [ABSTRACT FROM AUTHOR]

Abstract Currently, haze events in winter occur more frequently than decades ago, especially in Eastern and Central China, including the Yangtze River Delta (YRD). WRF-Chem is applied in this study to explore the discrepancies of the simulated air pollutants induced by employing different emission inventories, particularly during haze events. Two inventories are involved in this study, MEIC (Multi-resolution Emission Inventory for China) and GlobEmission (inventory from GlobEmission project emission estimates), representing the emission inventories based on quite different ways. We first compared monthly emissions of SO 2 and NO x in two inventories during January over YRD, and found the mean differences of SO 2 (NO x) are 20% (7%), with the ranges of 0%–47% (−100%–100%). The largest differences are both found in Shanghai, with 47% for SO 2 (MEIC in 2010, GlobEmission in 2014), and 45% for NO x (MEIC in 2010, GlobEmission in 2015), respectively, partly because the reduction of emissions was large during the 12th Five-Year Plan (2011–2015) in this area. By comparing the simulated air pollutants mass concentrations using two inventories with in-situ observations during January 2015, we found that the simulated SO 2 using MEIC and GlobEmission are both higher than observations, with mean normalized biases of 207% and 121% over YRD, respectively, and much larger in the city cluster of Nanjing-Shanghai, where are nearly four (MEIC) and three times (GlobEmission) higher than observations. In contrast, NO 2 simulations using GlobEmission are lower than observations (22%) and MEIC simulations (45%) over YRD. The largest biases of GlobEmission simulation are found in Zhejiang province (over 70%). The biases of simulated monthly mean PM 2.5 are 38% (MEIC) and 30% (GlobEmission) over YRD, respectively. A case study during heavy haze event shows that the biases increase to 61% (MEIC) and 39% (GlobEmission), and spatial correlation coefficient in the simulation using GlobEmission increase to 0.69 over YRD. Temporal correlation coefficients in the city cluster of Nanjing-Shanghai increase from 0.5 (MEIC) to 0.7 (GlobEmission). Highlights • Emissions of SO 2 (NO x) in MEIC inventory are 20% (7%) higher than GlobE. • Simulated SO 2 (NO 2) are 207% (49%) in MEIC and 121% (−22%) in GlobE. than observed. • Simulated PM 2.5 are 38% (MEIC) and 30% (GlobE.) higher than observations. • Larger discrepancies (22%) of simulated PM 2.5 by two inventories are found in haze. • Temporal R of PM 2.5 is better from simulation with GlobE. (0.7) than with MEIC (0.5). [ABSTRACT FROM AUTHOR]