Abstract. Co‑occurring PM2.5 and O3 pollution (Double High Pollution, DHP) presents a growing air quality concern, yet its response to emission controls under different meteorological conditions is poorly quantified. Previous studies have focused on synoptic‑scale or single meteorological variable with limited consideration of aerosol-meteorology feedback. This study investigated the DHP response to NOx–VOC emission reductions and the role of aerosol-meteorology feedback in Beijing–Tianjin–Hebei-Shandong in July 2020 using the WRF-Chem model. DHP occurred under warm (23–31 °C), moderately humid (45–80 %), and shallow boundary layer (0.5–1.1 km) conditions. These typical meteorological conditions were classified into five types: Convective, Stable, WarmHumid, DryHot, and Moderate based on boundary layer height, temperature and humidity. PM2.5 was highest under Stable and WarmHumid (~46 μg m-3) and lowest under DryHot (~40 μg m-3); O3 was highest under DryHot and Moderate (~83–84 ppb) and lowest under WarmHumid (~76 ppb). O3 responded most strongly to emission controls under WarmHumid (-25.2 % at 50 %/50 % NOx/VOC) and weakest under DryHot (-23.3 %) with stronger NOx sensitivity under WarmHumid and Convective; PM2.5 reductions were largest under Stable and WarmHumid (-20.9 % and -20.6 %) and smallest under DryHot (-15.2 %). Aerosol feedback enhanced PM2.5 reduction most under Stable and WarmHumid but weakened O3 reduction under Stable when NOx cuts were large without VOC co-reduction, driven by the most lasting PBLH increase and strong OH amplification. These results demonstrate that meteorological condition-specific strategies, particularly coordinated NOx–VOC control under stagnant and humid conditions, are essential for effective DHP mitigation.

China's effective control of PM2.5 has coincided with increasing ozone concentrations and frequent compound pollution of PM2.5 and O-3 (co-pollution), indicating the critical role of aerosols in air pollution. To investigate the impact of aerosol extinction on ozone (O-3) and secondary organic aerosols (SOA), the Community Multiscale Air Quality (CMAQ) model, incorporating an enhanced Mie scattering parameterization, was applied to simulate a co-pollution episode (25-30 September 2022) in the Beijing-Tianjin-Hebei region. The improved single-scattering albedo (mean bias decreased by 87%) provided a robust foundation for aerosol extinction assessment. During the episode, aerosol extinction suppressed surface photolysis rates, reducing regional surface O-3 concentrations by 2.59 +/- 4.6% and increasing SOA concentrations by 3.40 +/- 9.90%. Although SOA increased across the region, it decreased in heavily polluted Beijing under the influence of aerosol extinction, exhibiting variations consistent with those of O-3. Process analysis in Beijing indicated that within the planetary boundary layer (PBL), aerosol extinction lowered O-3 by suppressing daytime chemical production and inhibiting SOA formation mainly by suppressing aerosol-phase conversion, with the strongest effect occurring near the surface and gradually diminishing with altitude. However, at the top of the PBL, aerosol extinction resulted in elevated O-3 and SOA levels, strengthening vertical gradients and promoting downward transport, which offset similar to 70% of the decrease in O-3_CHEM and SOA_AERO below 440 m at midday. Furthermore, aerosol extinction led to greater decreases in pollutants in urban regions than in suburban areas, narrowing the urban-suburban concentration gap and thereby suppressing the outward transport of urban pollutants. The quantified role of aerosol extinction in O-3 and SOA formation and transport provides critical insights into how declining aerosol concentrations may reshape future regional air quality.

In this study the emission-driven aerosol-meteorology feedback on winter PM2.5 variations in Central and East China (CEC) from 2014 to 2020 is examined by applying the Weather Research and Forecasting (WRF) model coupled with Chemistry. Emission reductions from 2014 to 2020 led to increases in downward shortwave radiation at the surface, 2-m temperature and planetary boundary layer height, as well as decreases in 2-m relative humidity in the central part of CEC. This also led to an increase in precipitation and a decrease in the liquid water path in the southern part of CEC. The change in the PM2.5 concentration induced by aerosol-meteorology feedback (Delta M PM2.5) was-10 mu g m-3 to-5 mu g m-3 in eastern Hunan Province and-5 mu g m-3 to-2 mu g m-3 in other CEC regions. The average percentages of Delta M PM2.5 relative to the total change in the PM2.5 concentration due to emission reduction were 9.8 %, 5.7 %, 5.6 %, 9.2 %, and 20.3 % for CEC, the Beijing-Tianjin-Hebei (BTH), the Fenwei Plain (FWP), the Yangtze River Delta (YRD) and the Pearl River Delta (PRD), respectively. In the central part of CEC, the contribution of aerosol-radiation interaction (ARI) dominated, with a very slight contribution from aerosol-cloud interaction (ACI). In the southern part of CEC, both ACI and ARI contributed to Delta M PM2.5, with ACI playing a more significant role. Delta M PM2.5 exhibited distinct diurnal variations in the BTH, the FWP, and the YRD, with greater contributions during the day and smaller contributions at night, which was attributed primarily to the diurnal variation in the ARI contribution. This study emphasizes the necessity of considering aerosol-meteorology feedback when assessing the effects of emission control measures on PM2.5 concentrations.

Abstract. Air-pollution mitigation can reduce Arctic black carbon (BC) deposition and thereby slow Arctic warming by weakening snow darkening and associated radiative effects. Yet the regional land-surface benefits of future BC-deposition reductions across contrasting mitigation pathways remain poorly quantified. We use Polar-WRF coupled online with the SNICAR snow-albedo scheme to isolate the effects of spatially heterogeneous BC deposition associated with low-emission (SSP1-2.6), intermediate-emission (SSP2-4.5), and high-emission (SSP5-8.5) pathways during 2046–2050. All experiments share an SSP2-4.5 large-scale meteorological background and differ only in prescribed BC deposition; they therefore quantify the BC-deposition pathway rather than the full climate response to each SSP. Relative to simulations without BC in snow, Arctic-mean spring snow albedo decreases by 16.9 %, 18.1 %, and 19.3 % under SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. BC deposition increases surface net radiation by 1.0–1.4 W m−2, spring snowmelt by 4.1–6.1 mm d−1, advances snow disappearance by 6.7–7.6 days, and increases Arctic land-mean near-surface air temperature by 0.09–0.12 °C. Compared with SSP5-8.5, the 39.6 % lower BC deposition under SSP1-2.6 produces 12.4 %, 28.6 %, and 32.8 % smaller BC-induced changes in snow albedo, net radiation, and snowmelt, respectively. Responses are regionally heterogeneous: Greenland shows the strongest albedo and snowmelt responses despite relatively low deposition, whereas the European Arctic and West Siberia show stronger radiative responses, where substantial snow darkening coincides with greater incoming solar radiation. These results provide a process-based, regional evaluation of the deposition-related Arctic climate benefits of mid-century mitigation pathways.

In recent years, tropospheric ozone (O3) pollution has become an increasingly serious issue in China. Elevated concentrations persist during the summer, not only in the densely populated eastern regions but also in the sparsely populated western regions. In this study, a rapid emission optimizing method was developed and applied using the Regional Atmospheric Modeling System-Community Multi Scale Air Quality (RAMS-CMAQ) modeling system. Satellite observations were then integrated to optimize nitrogen oxide (NOx) and volatile organic compound (VOC) emissions over the Qinghai and Beijing-Tianjin-Hebei (BTH) regions. Based on the improved emission inventory, a numerical source apportionment model was employed to estimate the source contributions of tropospheric O3 in both regions during June 2024. Results showed that O3 levels in the BTH region were primarily influenced by industrial and traffic emissions, as well as anthropogenic emissions transported from neighboring cities. In contrast, anthropogenic contributions in Qinghai were significantly lower, while natural sources and long-range transport played a more dominant role, underscoring their critical importance in this region. Because the high O3 burden in Qinghai results from multiple interacting factors, pollution control is more challenging, and thus requires a more refined and comprehensive governance strategy.

As a typical coastal city in northern China, Tianjin often suffered severe co-pollution of PM2.5 and ozone (O3), with maximum daily 8h average ozone concentration (MDA8 O3) >= 160 mu g m-3 and the daily mean PM2.5 concentration (PM2.5_24h) >= 35 mu g m-3 during the warm season (April-September). However, the meteorological characteristics and formation mechanism of co-pollution in Tianjin remain unclear. In this study, the temporal variations of co-polluted days in Tianjin were analyzed based on the observation data from 2017 to 2024. Two typical episodes were selected to investigate the meteorological influence and driving factors of co-pollution by using Community Multiscale Air Quality (CMAQ) model. The number of co-polluted days fell during 2018-2021 and rose to 35 days in 2024, which accounted for 45.5% of the total polluted days during the warm season in Tianjin. The MDA8 O3 on co-polluted days was higher than that on O3-single polluted days, highlighting the role of intensified atmospheric oxidation. Co-pollution is prone to occur on the days with high temperature and relative humidity, weak wind or sea-land circulation and temperature inversion. Two formation mechanisms of co-pollution in Tianjin were investigated. The O3-initiated co-pollution on June 27, 2020 was characterized by a rise in O3 firstly and a subsequent increase in PM2.5. Process analysis and source apportionment simulations revealed that aerosol chemistry was the main source of secondary inorganic aerosols, and local production was the largest regional source to MDA8 O3 in Tianjin, with an average contribution of 21.76%, suggesting that local chemical production under strong atmospheric oxidation is the primary driver of O3-initiated co-pollution. In contrast, the PM2.5-initiated co-pollution on April 8, 2022 began with elevated PM2.5, which was attributed to regional transport and accumulation caused by unfavorable meteorological conditions (sea-land breeze and temperature inversion). Subsequently, O3 increased as precursors transported from Shandong (25.57%) and other adjacent regions (23.56%). This study emphasized the importance of reducing atmospheric oxidation and regional collaboration for co-pollution prevention and control in northern coastal areas.

The quantitative effect of aerosol-cloud interactions (ACI) on O3 concentrations remains unclear after implementation of anthropogenic emission controls in recent years. This study investigates the effects of the aerosol-radiation interactions (ARI) and ACI on O3 concentrations during four PM2.5-O3 compound pollution episodes (Episode 1: 19-26 July 2019, Episode 2, 25 April to 2 May, Episode 3: 20-26 September, and Episode 4: 20-29 September 2022) in Beijing-Tianjin-Hebei region (BTH) and Yangtze River Delta (YRD) by conducting sensitivity experiments using the USTC-version WRF-Chem model with an embedded integrated process rate (IPR) analysis scheme. Both ARI and ACI reduce daytime surface downward shortwave radiation (SWDOWN), but their spatial influence varies: ARI has a stronger effect in the BTH (2-89 W m-2) than in the YRD (1-42 W m-2), whereas ACI exerts a more substantial reduction in the YRD (2-89 W m-2) than in the BTH (1-45 W m-2). This reduced SWDOWN disrupts the near-surface energy balance, leading to lower 2-m temperatures, higher humidity, weakened convection, and a decreased planetary boundary layer height (PBLH). For liquid water path, ACI increases it (4-128 g m-2) which is much larger than the decrease (2-34 g m-2) induced by ARI. ARI and ACI consistently increase PM2.5 concentrations but decrease O3 levels. Averaged at all the compound pollution sites of the four episodes, ARI elevates PM2.5 more in the BTH (6.1 & micro;g m-3, 15.7%), while ACI-induced PM2.5 enhancements are more pronounced in the YRD (7.0 & micro;g m-3, 16.6%). The corresponding ARI-induced O3 reduction is 4.6 & micro;g m-3 (5.4%) in the BTH and 2.3 & micro;g m-3 in the YRD (2.9%), while the ACI-induced O3 reduction is more extensive in the YRD (4.9 & micro;g m-3, 6.3%) than that in the BTH (2.6 & micro;g m-3, 3.1%). ARI exerts a more pronounced influence in more polluted regions, while ACI has a more significant impact in cloud-covered areas. ARI and ACI significantly suppressed photolysis rates (J[O1D]) while elevating precursor (NOx, NOy, and VOCs) concentrations indicating that the suppression of photolysis rates and chemistry production as the primary reason for O3 reduction, which is also confirmed by IPR analysis. The IPR results further indicated that vertical mixing reduced surface O3 by facilitating its transport to middle altitudes. The study concludes that under future strict emission controls, the reduction in PM2.5 may weaken ARI and ACI, potentially increasing O3 concentrations predominantly clear sky or lightly clouded meteorological scenarios. Therefore, strengthening controls on O3 precursors and optimizing regional joint prevention mechanisms are crucial to counteract this risk.

Clouds strongly influence the Arctic surface energy balance, and the choice of microphysics scheme significantly affects simulations of clouds and their radiative impacts, especially during polar nights. In this study the winter Arctic clouds and their radiative effects were investigated using three cloud microphysics parameterization schemes (single-moment WSM6 and double-moment Morrison and Thompson schemes) implemented in the polar-optimized Weather Research and Forecasting (PWRF) model in 2020. The effects of these three schemes on Arctic clouds and their radiative effects have not been comprehensively evaluated in previous studies. Simulations are compared with CloudSat retrievals and ground-based radiation measurements from Barrow, Greenland Summit, and Ny-& Aring;lesund. The results indicate that although all the schemes generally reproduce the spatial and vertical structure of Arctic clouds, systematic biases exist. The WSM6 scheme significantly underestimates the cloud fraction (0.23 vs. 0.32 observed) and liquid water content (LWC: 1.3 mg g-3 vs. 5.5 mg g-3), especially in mid- and high-level clouds, largely because of excessively rapid ice particle sedimentation. The Thompson scheme overestimates cloud fraction (0.41), LWC (2.9 mg g- 3), ice water content (IWC: 14.3 mg g- 3 vs. 10.4 mg g- 3), and downward longwave radiation (DLR), leading to substantial surface warming (bias up to +42.7 W m- 2). The Morrison scheme performs best overall, agreeing well with the observed downward shortwave radiation (DSW bias: - 1.5 W m- 2 at Barrow) and cloud fraction (0.39). The LWC can significantly influenced cloud optical thickness and longwave emissivity, highlighting the importance of accurately representing the liquid phase for surface radiation balance. The type of precipitation (rain vs. snow) and its accumulation varied notably among the schemes, affecting snow depth simulations by as much as 0.1 m, with the Thompson scheme favoring solid precipitation and the Morrison and WSM6 schemes producing more rainfall. The study concludes that the Morrison scheme is the most appropriate for Arctic surface energy balance research, although all the schemes require improvements in representing mixed-phase cloud microphysics, particularly in the vertical distribution of IWC and absolute LWC values, to reduce uncertainties in Arctic climate projections. Despite these limitations, this study demonstrates the use of Polar-WRF with the Morrison scheme for Arctic climate simulations, providing a foundation for future Arctic surface energy balance simulation research.

Global climate change has become a significant challenge facing human society,with greenhouse gas(GHG)emissions being one of its primary driving factors.To effectively assess global progress in greenhouse gas reduction,the Paris Agreement established the Global Stocktake(GST)mechanism,which requires independent verification of national GHG emission inventories.However,existing"bottom-up"emission inventories face significant uncertainties in compilation methods,data completeness,and verifiability,making it difficult to meet the need for high-precision,standardized carbon emission data for the GST.Therefore,it is urgent need to develop"top-down"satellite monitoring methods to enhance the transparency and scientific rigor of carbon emission inventory verification. This study focused on the requirement analysis and design for China's next-generation carbon monitoring satellite(TanSat-2).Based on Observing System Simulation Experiments(OSSE),an evaluation framework for satellite program assessment was constructed,and a carbon satellite design proposal aimed at GST needs is presented.First,a satellite verification technology system for GHG emission inventories at multiple scales("global-regional-hotspot")was established,defining key technological requirements for inventory verification at different scales and proposing uncertainty constraints for the next-generation carbon satellite inventory verification.Second,a multi-component synergistic observation and anthropogenic emission source separation technology system was developed by integrating"greenhouse gases-pollutant gases-vegetation fluorescence".The system was applied to evaluate the ability of satellite carbon monitoring to distinguish between ecosystem carbon cycles and anthropogenic carbon emissions,leading to the development of a scientific product indicator system.Additionally,we assessed the impact of various payload technical parameters(spectral resolution,signal-to-noise ratio,wavelength range,etc.)on the retrieval errors of each observation element.The technical indicators for GHG,pollutant gas,aerosol,and Solar-Induced Chlorophyll Fluorescence(SIF)detection payloads was qualified,aligned with the engineering capabilities and constraints of satellite platforms and payloads. Based on this,a design for the TanSat-2 platform was completed,incorporating multi-component,high-temporal,multi-scale,and high-precision capabilities.A mid-orbit elliptical frozen sun-synchronous orbit satellite scheme was proposed.The TanSat-2 was designed to equipped with three advanced effective payloads:(1)the Ultra-wide-field Carbon Pollution collaborative monitoring Instrument(UCPI),(2)the Hotspot Greenhouse gas Emission Tracker(HGET),(3)the Cloud Aerosol Polarization Imager(CAPI).With a coverage capacity of up to a thousand kilometers,UCPI was designed for monitoring carbon emissions at the national and regional scales.HGET was specially designed for detailed monitoring of major emission sources,capable of high spatial resolution for hotspot area investigation.And CAPI was designed to optimize GHG inversion accuracy and reduce the uncertainty of aerosol scattering in the remote sensing inversion of CO2,CH4,and other gases.

Ozone (O3) pollution, which not only depends on emissions but is also closely related to prevailing meteorological conditions, is a major concern in China. In the context of China's emission controls imposed during the 13th Five-Year Plan, the variations of synoptic circulations and associated summertime O3 variations were investigated during 2016-2020. Different from the common use in previous studies of observations and numerical models, a regional atmospheric composition reanalysis dataset at a refined spatial (45 km) and temporal (1 h) resolution was applied here, in which O3 and its major precursors, emissions, and meteorology were jointly assimilated to reduce the impacts of uncertainty. With this continuous and optimal dataset, the impacts of regional synoptic variations on O3 interannual variability were explored through an objective circulation classification approach during 2016-2020 over China. On the one hand, from the perspective of O3 variability, increasing trends of O3 levels were detected. Compared to the Yangtze River Delta (YRD), Pearl River Delta (PRD), and Sichuan Basin (SCB) with less summer pollution, the Beijing-Tianjin-Hebei (BTH) and Fenwei Plain (FWP) regions had more severe summer O3 pollution with the frequency of days exceeding Grade 3 tends to be around 50%. On the other hand, from the perspective of O3 variability driven by meteorological conditions, obvious interannual variations of synoptic circulation patterns occurred, and about half of type C occurrences were accompanied by O3 pollution episodes in BTH and FWP (i.e., 52.73% and 45.65%), while far fewer pollution episodes occurred with type C in YRD and PRD (i.e., 2.78% and 0.59%). In addition, according to the quantitative assessment of the meteorological contribution, the contribution of interannual variations of synoptic circulations to changing the O3 variability amounted to 13%-31% in BTH, YRD, PRD, FWP, and SCB. Therefore, the interannual variability of O3 from 2016 to 2020 over China was closely linked with the regulations of O3 precursors. This work provides an understanding of O3 variation under the impacts of emission regulations and meteorological conditions over China.

The rapid warming of the Arctic, driven by glacial and sea ice melt, poses significant challenges to Earth's climate, ecosystems, and economy. Recent evidence indicates that the snow-darkening effect (SDE), caused by black carbon (BC) deposition, plays a crucial role in accelerated warming. However, high-resolution simulations assessing the impacts from the properties of snowpack and land–atmosphere interactions on the changes in the surface energy balance of the Arctic caused by BC remain scarce. This study integrates the Snow, Ice, and Aerosol Radiative (SNICAR) model with a polar-optimized version of the Weather Research and Forecasting model (Polar-WRF) to evaluate the impacts of snow melting and land–atmosphere interaction processes on the SDE due to BC deposition. The simulation results indicate that BC deposition can directly affect the surface energy balance by decreasing snow albedo and its corresponding radiative forcing (RF). On average, BC deposition at 50 ng g−1 causes a daily average RF of 1.6 W m−2 in offline simulations (without surface feedbacks) and 1.4 W m−2 in online simulations (with surface feedback). The reduction in snow albedo induced by BC is strongly dependent on snow depth, with a significant linear relationship observed when snow depth is shallow. In regions with deep snowpack, such as Greenland, BC deposition leads to a 25 %–41 % greater SDE impact and a 19 %–40 % increase in snowmelt compared to in areas with shallow snow. Snowmelt and land–atmosphere interactions play significant roles in assessing changes in the surface energy balance caused by BC deposition based on a comparison of results from offline and online coupled simulations via Polar-WRF and the community Noah land surface model (LSM) with multiple parameterization options (Noah-MP) and SNICAR. Offline simulations tend to overestimate SDE impacts by more than 50 % because crucial surface feedback processes are excluded. This study underscores the importance of incorporating detailed physical processes in high-resolution models to improve our understanding of the role of the SDE in Arctic climate change.

Top‐down methods commonly use atmospheric CO 2 concentration observations to constrain carbon source and sinks. Despite the increase in spaceborne and ground‐based concentration measurements, atmospheric inversions are usually limited by uncertainties in chemical transport models (CTMs) when relating fluxes to observed CO 2 mole fractions. CO 2 eddy covariance (EC) flux measurements have been widely used to directly measure CO 2 fluxes over various ecosystems, but they have rarely been used as constraints in top‐down estimations. In this study, we focused on the development of a novel fluxes assimilation scheme through direct flux observations within an Ensemble Square Root Filter assimilation framework. The assimilation scheme avoided some of complexities of concentration observation assimilations. The methodology was primarily applied to typical regions in west China, taking advantage of eight long‐term ecosystem EC sites. Moreover, four sets of assimilation experiments were designed to quantify the impacts of observational constraints by flux and concentration measurements. Generally, results indicate that the monthly and hourly statistics of the a posteriori fluxes constrained by flux observations agreed well with flux measurements, demonstrating reasonable performance in seasonal and diurnal variations. Specifically, assimilation results demonstrated the advantage of a posteriori estimates inferred from flux measurements during growing season, as compared to results inferred from concentrations, while some limitation still exists in monthly budget estimates. Nevertheless, it is important to note that current results are only a mathematical optimum. CO 2 biospheric fluxes can be estimated more reliably and robustly at the regional scale given considerably more flux observations for efficient constraint.

The spatiotemporal variation characteristics of simultaneous high pollution (SHP) from surface fine particulate matter (PM2.5) and ozone (O3) were explored on the basis of hourly observation data in 1092 sites from 2015 to 2023 across China. Statistics show that the frequency and intensity of SHP days are highest in North China, followed by South China. Seasonally, SHP days occur more frequently in spring and autumn across China, accounting for 43% and 27% over nine years, respectively. The number of SHP days across national sites first decreased (by 48.63% from 2015 to 2021) but then increased (by 49.37% from 2021 to 2023). Gridded PM2.5 component data were analyzed to explore these variations. Despite reductions in anthropogenic emissions, the proportion of secondary inorganic components in PM2.5 increased by 4.51%, 3.6%, and 5.41% in Beijing-Tianjin-Hebei, FenWei Plain, and Yangtze River Delta on SHP days from 2015 to 2021. Concurrently, the detrended PM2.5 and O3 concentrations showed significant positive correlations in most regions. These phenomena may be related to high O3 concentrations and meteorological conditions. The weather analysis reveals that specific weather conditions contribute to the occurrence of SHP, with notable spatial variations: cyclonic low-pressure systems with high temperatures in northern China, anticyclonic high-pressure systems with high temperatures and low humidity in central China, and east winds in southern China. These conditions may promote secondary pollutant formation and hinder pollutant dispersion. In addition, the increase in primary PM2.5 on SHP days after 2021 underscores the importance of controlling anthropogenic emissions. Overall, a comprehensive approach is essential to tackle the SHP challenge.

The impact of aerosol-meteorology feedback on the effectiveness of emission reduction for PM2.5: A modeling case study in Northern China Meigen Zhang, Jing He, and Yi GaoState Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China The quantification of the effectiveness of anthropogenic emission control measures is crucial for future air quality policies. Meteorology plays a vital role in haze pollution, and the interactions between aerosol and meteorology have been widely studied. However, it is not fully clear how aerosol-meteorology feedback affects the effectiveness of emission reduction for PM2.5, which limits our ability of optimizing anti-pollution policies. Here, with the two-way atmospheric chemical transport model WRF-Chem, the effects of aerosol-meteorology feedback on the effectiveness of emission reduction for PM2.5 during a winter severe haze event in 2016 over the Northern China Plain (NCP) are studied. In the more polluted area of NCP (MP_NCP) during the daytime, 20% emission reduction over NCP increases near-surface downward shortwave radiation by 4.62 W/m2, 2 m temperature by 0.08 ◦C, boundary layer height by 7.19 m and reduces 2 m relative humidity by 0.31% and thereby alleviates worsened meteorological conditions caused by aerosol effect. As a result, in MP_NCP, 20% emission reduction without aerosol-meteorology feedback leads to a decrease of 40.49 μg/m3 of near-surface PM2.5 and the above meteorological changes decrease near-surface PM2.5 concentration by 7.82 μg/m3, indicating that aerosol-meteorology feedback strengthens the effectiveness of emission reduction by 19%. In the less polluted area (LP_NCP), aerosol effect induced meteorological changes decrease PM2.5 concentration by 7.57 μg/m3 and 20% emission reduction without aerosol-meteorology feedback leads to a decrease of 13.15 μg/m3 in near-surface PM2.5. This reveals a remarkable enhancement of 58% in the effectiveness of emission reduction, which is much larger than that in MP_NCP. Such difference can be attributed to the presence of more clouds in LP_NCP, where the decrease in liquid water path, along with the increase in the planetary boundary layer height, jointly contributes to the PM2.5 decrease. Moreover, the effect of aerosol-meteorology feedback on the effectiveness of emission reduction for PM2.5 is nonlinear. With increasing PM2.5 concentration, the aerosol-meteorology feed back induced PM2.5 reduction first increases and then stabilizes once the PM2.5 concentration exceeds 350 μg/m3. This study can provide reference for air pollution control strategies. Keywords: Emission reduction, Aerosol-meteorology feedback, WRF-Chem

Forest fire emission inventories constitute a fundamental data source for air quality modeling and investigations into the environmental impacts of forest fires. Current datasets predominantly offer daily or monthly emission estimates derived from Moderate Resolution Imaging Spectroradiometer (MODIS) fire products. However, detailed analyses of forest fire emission characteristics at the hourly resolution remain limited in China. In this study, we developed an hourly emission inventory for Chinese forest fires from 2016 to 2022 by integrating MODIS, Visible Infrared Imaging Radiometer Suite (VIIRS), and Himawari-8 active fire data, while incorporating diurnal variation patterns of forest fire activity. Utilizing this inventory, the spatial and temporal distributions of forest fire emissions across China were analyzed. The findings indicate that forest fires consumed approximately 95,495 Gg of dry matter during the study period, with the majority concentrated in the southern, southwestern, and northeastern forest regions, accounting for 44.3 %, 32.5 %, and 18.2 % of the total emissions, respectively. Emission hotspots in the southern forest region were predominantly located in provinces, such as Guangxi, Guangdong, Fujian, Jiangxi, and Hunan. In the southwestern forest region, hotspots were concentrated in southern Yunnan and Sichuan, while in the northeastern forest region, they were distributed across northeastern Inner Mongolia, northwestern Heilongjiang, and central Jilin and Liaoning. Months with higher forest fire emissions in China occur in February, March, April, October, November, and December. Significant seasonal differences exist in the diurnal variation of fire emissions across different forest regions. The Northeast Forest Region exhibits distinct emission peaks during spring and autumn, with peak times occurring at 13:00 and 12:00, respectively. The Southern and Southwest Forest Regions show similar diurnal variation characteristics. During winter, their peak emission times occur at 13:00-14:00 and 14:00-15:00, respectively. In spring and autumn, both regions display two emission troughs and three emission peaks. The occurrence times of these troughs and peaks in the Southwest Forest Region lag approximately 1 h behind those in the Southern Forest Region. These results provide critical insights into the spatiotemporal characteristics of forest fire emissions in China and offer a robust foundation for further research on the precise quantification of the air quality impacts associated with forest fires.

At present, inversions at higher temporal and spatial resolution tend to be in increasing demand. The resolution and precision of methane (CH4) inversions depend on quality of observations, transport model, and inversion scheme. Currently, most inversion-based estimates of CH4 in China use a global atmospheric transport model to relate emissions to observations, or a Lagrangian model to quantify the receptor-to-source sensitivity. Taking advantage of the ability that regional transport models have in mesoscale simulation, a regional inversion system was developed to infer China's CH4 emissions. The CMAQ (Community Multi-scale Air Quality) model was configured for forward simulation of CH4, including processes of emission, transport, diffusion, and chemical transformation. Furthermore, the Ensemble Kalman Smoother was extended to assimilate satellite observations with joint optimization scheme of concentrations and emissions to reduce the impact of initial uncertainty. We found that the posterior annual estimated emissions (53.30 Tg a -1 ) in 2020 were closer to the official reported figure (53.57 Tg a -1 ) than to the prior (63.80 Tg a-1 ), with a downward correction of 16.45% in prior estimates based on extrapolation of the bottom-up inventory, which likely led to overestimation. Moreover, under current observational coverage, monthly posterior estimates reflected region-dependent responses to local conditions. Generally, the regional assimilation system estimated annual and monthly CH4 emissions well, attributable to reliable CMAQ simulation, joint assimilation scheme, and careful selection of satellite retrievals. In addition, evaluation of the posterior estimates indicates that inversion delivers reasonable improvements, but amelioration of uncertainties in prior information and observations is still needed.

The chemical element composition of dust particles was characterized by the ground-based samples collected at Beijing in the spring of 2004. Most of mineral and pollutant element concentrations in particles were elevated in dusty days, about 2-4 times higher than the levels in non-dusty days. Each of Si, Ca, Fe and Al accounted for over 10% of the sums of total 20 elements in mass, for example, Si was in 44.3%, 38.7% for dusty and non-dusty cases, respectively. Si, Fe, Ni or Ti can be used as an indicator of dust outflow, and Cu can be viewed as an evidence of dust particles mixing with anthropogenic contaminants as a result of coagulation processes. Mineral and pollutant elements showed a bimodal distribution in the mass particle-size distributions in both dusty and non-dusty days, but their peak concentrations fell in different size stages. Zn, Cl and Cu were mostly enriched in fine particles, Pb was enriched in intermediate sized particles, but most mineral elements, S and part of Cu were enriched in coarse particles. Mineral elements were dominated by crustal material, and pollutant elements were from non-crustal material including local and remote sources. Among the crustal material, part of Ca was originated from local construction activities. High concentration of Cu was related to the of rapidly increasing vehicles in Beijing, and the replacing of coal with diesel oil for heating fuel. Most of the mineral dust particles sampled at Beijing were originated from the Mongolian sandy soil and the Chinese loess in the spring of 2004. Using Mg/Al ratio element tracer technique method, the aerosol from outside Beijing accounted for 66.3% and 88.6% to the total mineral aerosol during dust event on 10-11 March and 28-30 March 2004, respectively.

The 26th United Nations Climate Change Conference (COP26) proposes to limit global warming to <1.5 degrees C above pre-industrial levels until 2030 and aligns CO2 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 degrees C, 1.4 g kg(-1), and 0.8 m s(-1), respectively. Additionally, the WRF-Chem overestimates PM2.5, 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.

To understand the ionic and elemental components in PM2.5 over eastern coastal areas in China, aircraft measurements were carried out from 25 December, 2002 to 6 January, 2003. PM2.5 filter samples were collected and analyzed for mass concentrations, nine ionic components and 15 trace elements. The highest concentrations of PM2.5 were observed at the lowest altitudes, indicating the influence of ground-level sources. Sulfate, nitrate and ammonium were the main water-soluble components, and the sum of their concentrations accounted for 60-70% of the total ionic mass. Cl-depletion was observed to an extent of 3-26%. Anthropogenic ally derived particles contributed about 60% of the total measured ionic components, and natural sources such as sea salts and soil dusts contributed another 40%. Different to many earlier studies, Ca2+ was found to mainly originate from anthropogenic sources. PM2.5 showed an acidic nature, with a neutralization potential/acidic potential ratio of less than 1.0. NH4+ was the major neutralizer of aerosol acidity. A good correlation was observed between the concentrations of NO3- and nssCa(2+), suggesting that photochemically produced HNO3 was partly absorbed by mineral particles. S had the highest concentrations among the 15 elements. The enrichment factor values of the observed elements were all over 1, indicating that all of them were influenced by anthropogenic sources. The enrichment factors of Pb, S, As, Cu and Zn were over 100, and suggesting that they were greatly enriched.