The Tibetan Plateau (TP) plays a pivotal role in the Asian climate system, yet it is increasingly impacted by the long-range transport of anthropogenic air pollutants from surrounding regions. Sulfur dioxide (SO2) and sulfate aerosols are of particular concern due to their profound effects on the regional radiation budget, cloud microphysics, and the cryosphere. However, their source contributions over the TP remain poorly constrained. In this study, a source-oriented WRF-Chem model is applied to quantify the contributions of SO2 and sulfate aerosols over the TP during the Asian summer monsoon period (June-August 2012). Sulfur species are categorized into regional tracers representing emissions from East Asia, South Asia, West Asia, Southeast Asia, and Central Asia, with an additional background component representing the well-mixed global atmosphere. Model performance is evaluated against satellite retrievals and ground-based observations. Results show that the TP SO2 is primarily driven by emissions from South Asia (43.6%) and East Asia (30.5%), complemented by a notable background contribution (18.7%). Sulfate burden is dominated by the background source (37.8%), followed by significant contributions from East Asia (27.3%) and South Asia (22.2%). The contrast between SO2 and sulfate highlights the critical role of chemical aging and long-range transport in shaping sulfate distributions over the TP. Overall, sulfur species distribution over the plateau reflects a complex interplay between regional anthropogenic emissions and large-scale background transport, strongly modulated by monsoon circulation and complex topography. These findings provide a robust quantitative framework for understanding sulfur transport mechanisms and their climatic implications over high-altitude environments.

The Tibetan Plateau (TP) plays a pivotal role in the Asian climate system, yet it is increasingly impacted by the long-range transport of anthropogenic air pollutants from surrounding regions. Sulfur dioxide (SO2) and sulfate aerosols are of particular concern due to their profound effects on the regional radiation budget, cloud microphysics, and the cryosphere. However, the relative contributions of regional emissions and large-scale background transport to sulfur species over the TP remain poorly constrained. In this study, a source-oriented WRF-Chem model is applied to quantify the contributions of SO2 and sulfate aerosols over the TP during June-August 2012. Sulfur species are tagged according to major surrounding source regions, including East Asia, South Asia, West Asia, Southeast Asia, and Central Asia, together with a background component representing sulfur species introduced through chemical initial and lateral boundary conditions. Model performance is evaluated against satellite retrievals and ground-based observations. Results show that the TP SO2 is primarily driven by emissions from South Asia (43.8%) and East Asia (30.5%), complemented by a notable background contribution (18.7%). Sulfate burden is dominated by the background source (37.8%), followed by significant contributions from East Asia (27.3%) and South Asia (22.2%). This contrast indicates that SO2 over the TP is more directly linked to regional emissions, whereas sulfate is more strongly influenced by secondary formation, chemical aging, wet removal, and large-scale background transport. These results emphasize the distinct behaviors of SO2 and sulfate and the importance of separating precursor transport from secondary aerosol formation over high-altitude regions.

Despite significant progress in reducing primary pollutants in China, surface ozone pollution remains a pressing challenge. This study evaluates the effectiveness of Empirical Kinetic Modeling Approach (EKMA) curves-constructed using Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), Observation-Based Model (OBM), and Empirical Kinetic Modeling Approach for Machine Learning (MLEKMA)- in guiding ozone control strategies in Luoyang. The levels of O3 and precursors between two sampling campaigns present two main paradoxes, that reduction in source inventory do not guarantee the decrease of atmospheric NOx and VOC levels, and reduction of precursors do not always result in O3 decline. As indicated by all three models, O3 sensitivity in Luoyang was in a transition regime in both 2019 and 2021. However, only the WRFChem model yielded ozone concentration predictions (169.0-171.0 mu g/m3) that closely matched the observed values (168.8 mu g/m3), while OBM and MLEKMA significantly underestimated ozone levels-the observed concentrations were 2-3 times and 3-5 times higher than the respective model predictions. Each method presents its limitations: the WRF-Chem model lacks real-time responsiveness, while OBM and MLEKMA exhibit deficiencies in accurately predicting ozone concentrations. Therefore, integrating multiple EKMA-derived methods is recommended for practical ozone management. By leveraging the timeliness of OBM and MLEKMA alongside the predictive accuracy of WRF-Chem, a rapid and robust multi-model approach for urban ozone sensitivity assessment can be achieved, enabling governments to formulate more effective ozone control policies.

Black carbon, an important component of atmospheric aerosols, has an impact on climate change. When deposited on snow and ice, it reduces surface albedo, accelerating melting and amplifying global warming. Here, we analyzed lake sediment records from China and found that existing bottom-up inventories underestimate black carbon emissions prior to the mid-twentieth century. We incorporated a black carbon emission enhancement scheme based on reconstructed historical biomass burning emissions into a numerical climate model to assess this underestimation. The simulations indicated that increased historical emissions enhanced spring and summer radiative effects north of 60°N, leading to regional surface warming and accelerated Arctic snowmelt. Although the response varies with the strength of emission enhancement, the findings suggested that historical BC emission biases could alter the simulated Arctic energy balance and climate evolution. These results highlight the need for improved constraints on historical BC emissions to better assess their climate impacts. Increases in historical black carbon emissions enhanced spring and summer radiative effects north of 60°N, leading to regional surface warming and accelerated Arctic snowmelt, suggests a study combining climate modeling with lake-sediment records from China.

The new threat from atmospheric Micro/nano-plastics (MNPs) emissions now reaches far beyond the conventional scope of plastic-related issues. While MNP horizontal transport within the planetary boundary layer is well understood, limited knowledge of their vertical transport in the free troposphere and stratosphere hinder comprehensive modeling of their global atmospheric circulation. Considering that the production rates of radioactive beryllium isotopes (7Be and 10Be) above the tropopause is over 100 times higher than that in the near surface atmosphere, if relatively high concentrations and ratios of 10Be and 7Be can be observed near the surface, this phenomenon can serve as a unique isotope "fingerprint" for the invasion of deep stratospheric air invasion. Here, we present evidence of MNPs at mass concentrations of 0.0059-0.11 μg/m³ detected in Lhasa, southern Tibetan Plateau. And through synchronous high-precision observations of 7Be and 10Be, we have discovered strong stratospheric air signals during periods of high MNP concentration. Through the atmospheric transport models to delineate the occurrence, magnitude, retention period, flux, and characteristics of MNP pollution driven by upper-atmospheric vertical circulation in this high-elevation region. We found that under special topographic and aerodynamic conditions, atmospheric vertical circulation will promote the accumulation of MNPs for the enrichment and redistribution of MNPs. This work reveals the importance of vertical circulation in the upper atmosphere in the dynamics of MNP circulation based on evidence from beryllium isotopes.

Quantitative source apportionment of dust constitutes an important prerequisite for the design and implementation of effective ecological restoration strategies. A source-tagged approach based on the WRF-Chem model is developed to quantify dust source contributions of dust over the North China Plain and surrounding areas (NCPs). Two severe sand and dust storms (SDS) during 15-18 and 27-29 March 2021 (3.15 SDS and 3.28 SDS, respectively) are simulated using the model as a case study to quantify dust contributions from sandy deserts, deserts, the Chinese Gobi, the Mongolian Gobi, and other wind-eroded areas. The results show that the Mongolian Gobi is the dominant contributor to dust mass over the NCPs in both events (86.9% and 67.0%, respectively). During the 3.15 SDS, although the Chinese Gobi is the dominant emission source (14.1 Mt., 57.8%), its contribution to dust mass over the NCPs is only 7.3% because prevailing northeasterly winds transport its dust toward the northwest. During the 3.28 SDS, the dust contribution of the Chinese Gobi increases markedly to 25.1% due to the westward shift of the Mongolian cyclone, facilitating the long-range transport of dust to the NCPs. The difference in source contributions between the two events is mainly attributed to variations in the synoptic circulation pattern. In contrast, the contributions from deserts, sandy deserts, and other wind eroded areas are minor. The results provide a scientific basis for the management and prevention strategies of regional dust sources, as well as the construction of ecological resilience.

By systematically reviewing over 10,000 publications using a large-language-model-based framework, we provide a large-scale quantitative assessment of the global research landscape on climate change’s impact on air quality. Particulate matter (PM) and ozone (O3) are the most frequently studied pollutants, with a notable shift in focus from O3 to PM in recent years. Temperature, as a primary climatic driver, profoundly influences air pollution by altering emissions, chemical reactions, and dispersion. We found that research is heavily concentrated in high- and middle-income regions, while studies remain scarce in low-income countries. The global distribution of research effort correlates more strongly with economic capacity than with the climate risk and health burden. Our findings highlight the need for more geographically representative and mechanistically detailed research, underscoring the importance of forging a more equitable and risk-informed global research agenda.

In agricultural regions of northern China, PM2.5 (particulate matter with aerodynamic diameter less than 2.5 μm) pollution driven by biomass burning remains a critical environmental challenge, yet uncertainties persist in source apportionment due to methodological limitations and insufficient multi-method validation in agriculturally intensive areas. This study synergistically applied receptor modeling (PMF, Positive Matrix Factorization), emission inventory, and WRF-Chem (Weather Research and Forecasting model coupled with Chemistry) simulations to quantify biomass burning contributions in Siping City, Jilin Province, using year-round PM2.5 compositional data (July 2021-June 2022) and localized emission parameters. The PMF model resolved six sources, identifying biomass burning as the dominant contributor (35.10 %), corroborated by emission inventory revisions incorporating satellite-derived burned area mapping (50.73 km2), which attributed 37.50 % of total PM2.5 emissions to biomass burning. WRF-Chem simulations, driven by the revised inventory, demonstrated that biomass burning sources collectively contributed 40.13 % to ambient PM2.5 during an episode, with residential and open burning of biomass accounting for 28.12 % and 12.01 %, respectively. These multi-method results consistently highlight biomass burning's dominance (one-third of total emissions). The findings necessitate prioritizing residential biomass emission controls through clean energy transitions, stricter enforcement of combustion regulations, and integrated management strategies to mitigate air quality degradation in agricultural regions.

Aerosol light absorption has been widely considered as a contributing factor to the worsening of particulate pollution in large urban areas, primarily through its role in stabilizing the planetary boundary layer (PBL). Here, we report that absorption-dominated aerosol-radiation interaction can decrease near-surface fine particulate matter concentrations ([PM2.5]) at a large-scale during wintertime haze events. A "warm bubble" effect by the significant heating rate of absorbing aerosols above the PBL top generates a secondary circulation, enhancing the upward motion (downward motion) and the convergence (divergence) in polluted (relatively clean) areas, with a net effect of lowering near-surface [PM2.5]. Furthermore, aerosol absorption of ultraviolet-wave light effectively reduces the photolysis of chemical species, i.e., aerosol-photolysis interaction, hindering ozone formation, reducing atmospheric oxidizing capability, and suppressing secondary aerosol concentrations. Our model assessment reveals that the synergetic two effects decrease near-surface [PM2.5] by around 7.4%, so the presence of light-absorbing aerosols can considerably alleviate particulate pollution during wintertime haze events. Such negative feedbacks to the aerosol loading should be considered in weather/climate prediction and health assessment models.

Nitrogen dioxide (NO2) exposure has rarely been explored with respiratory outpatient visits in China. The Generalized Additive Model (GAM) is first employed to assess health related risk (RR) with NO2 and PM2.5 exposure in Tianjin during the winter of 2019/2020. The RR of NO2 exposure is 1.018 (95 % CI: 1.012-1.024) on lag 3 days, while that of fine particulate matter (PM2.5) exposure is 1.005 (95 % CI: 1.002-1.008) on lag 4 days per 10 μg m-3. The Weather Research and Forecasting model coupled to Chemistry (WRF-Chem) model is used to simulate the pollutant concentrations to calculate the total number of excess visits (EN) in the North China Plain (NCP) in the 2019/2020 winter. The EN of NO2 exposure is 0.97 million (95 % CI: 0.67, 1.27), while for PM2.5 exposure is 1.19 million (95 % CI: 0.50, 1.84). With the anthropogenic emission mitigation, the PM2.5 concentration has dropped by 40.3 μg m-3 on average in the NCP, but the NO2 concentration has rebounded by 1.8 μg m-3. The emissions mitigation reduces NO2-related EN by 0.76 million (95 % CI: 0.53-0.98) and PM2.5-related EN by 1.11 million (95 % CI: 0.48-1.69). The findings underscore that reducing NO2 emissions could yield more substantial health benefits.

Atmospheric aerosols influence clouds and precipitation by aerosol-radiation interactions (ARIs) and aerosol-cloud interactions (ACIs). In this study, the synergetic effect of ARIs and ACIs on the development and precipitation of a mesoscale convective system (MCS) that occurred in the Guanzhong Basin (GZB) of central China have been examined using a cloud-resolving fully coupled weather research and forecasting model with chemistry (WRF-Chem). The model reasonably reproduces the temporal variation and spatial distribution of air pollutants, the hourly rain rate, and daily precipitation distribution against observations in the GZB. Sensitivity simulations are conducted under different aerosol scenarios by adjusting the anthropogenic emissions. When the ARI effect is not considered, the daily precipitation does not show an increasing trend with increasing aerosols in the GZB. This primarily reflects the effects of ACIs due to competition among convective clouds to available water vapor in the development of the MCS. ARIs exert two opposite effects on convection: a stabilizing effect to suppress convection and a lifting effect to foster convection, which counteract each other. When the lifting effect outweighs stabilizing effect, the updraft is enhanced, which increases precipitation in the GZB. However, the synergetic effect of ARIs and ACIs significantly suppress precipitation when the particulate-matter (PM) pollution is severe. Note that the synergetic effect consistently decreases the precipitation in the whole domain with increasing aerosols, but ARIs play a more important role in the decreasing trend of the precipitation with deterioration of PM pollution.

Atmospheric nitrogen dioxide (NO2) has shown periodic conspicuous pulses in the tropospheric column in March over the North China Plain during the past two decades. However, these repetitive pulses have never been reported, and their underlying causes remain unclear. Here, we present robust evidence to demonstrate that agricultural fertilization drives the early spring NO2 column increase. The fertilization-driven soil NOx (= NO+NO2) emissions, comparable to anthropogenic sources, exert complicated influences on regional air quality. They significantly reduce nocturnal and diurnal O3 concentrations in agricultural areas in early spring, distinct from the scenarios in summer, but increase fine particulate matter (PM2.5) concentrations via strongly enhancing nitrate aerosol formation. The impact also extends to urban areas, approximately half that of agricultural areas. These findings have increasing implications for coordinated control of PM2.5 and O3 under global warming. We thus suggest that reducing NOx emissions in croplands is essential to achieve better air quality in agricultural countries and regions.

Wildfires release large amounts of greenhouse gases into the atmosphere, exacerbating climate change and causing severe impacts on air quality and human health. In this study, based on a bottom-up approach and using satellite data, combined with emission factor and aboveground biomass data for different vegetation cover types (forest, shrub, grassland, and cropland), the dynamic changes in CO2 emissions from wildfires in China from 2001 to 2022 were analyzed. The results showed that between 2001 and 2022, the total CO2 emissions from wildfires in China were 937.7 Tg (522.6-1516.0 Tg, 1 Tg = 1012 g), with an annual average of 42.6 Tg (23.8-68.9 Tg). The CO2 emissions from cropland and forest fires were relatively high, accounting for 45 % and 46 % of the total, respectively. The yearly variation in CO2 emissions from forest and shrub fires showed a significant downward trend, while emissions from grassland fires remained relatively stable. In contrast, the CO2 emissions from cropland fires showed an upward trend, primarily in Northeast China. Hot spot analysis and geographically and temporally weighted regression (GTWR) models revealed significant spatial heterogeneity in emissions across vegetation types. Persistent hot spots of shrub and forest fires were located in Southwest and South China, while Northeast China experienced sporadic but extreme fire events. The GTWR model for shrub fire CO2 emissions exhibited the highest predictive performance (R2= 0.87), and climatic factors (particularly temperature and humidity) were the main influencing factors. Notably, the recent rise in cropland fire CO2 emissions in Northeast China is closely linked to region-specific straw-burning policies. The research results provide valuable references for atmospheric transport models, regional fire management, and national carbon accounting frameworks in the context of climate change.

Studying historical changes in the 2.5 mu m particulate matter concentration (PM2.5) can clarify the relationship between air pollution and socioeconomic development. Daily PM2.5 levels and meteorological data (2012-2022) for three large cities (Beijing, Shanghai, and Xi'an) at different development stages in different regions of China were used to construct a random forest (RF) model for estimating historical PM2.5 data for the period 1973-2011, a time period in which few measurements were made. The eigenvalue for visibility was the largest in the RF model; visibility explained 0.76-0.87 of the variance in PM2.5 for the three cities. The daily estimated PM2.5 was validated, with an R-2 of 0.654-0.780 and average absolute error of 11.52-31.73 mu g m(-3) in the model. PM2.5 concentrations predicted by the RF model for 2004-2011 were highly correlated with gravimetric measurements (R = 0.585, p < 0.01). We extensively validated the results of RF using manual weighing PM2.5 data, online monitoring concentration of PM10, and aerosol optical depth (AOD), demonstrating the accuracy of the model. Over the study period, the PM2.5 level first increased and then decreased in the three cities; however, the year at which the trend changed differed. We further explored the effects of urbanization and economic growth on PM2.5 levels by investigating the correlations between socioeconomic indicators and PM2.5. The magnitude of the permanent population of Beijing and gross regional production growth in Shanghai were both significantly positively correlated with the PM2.5 level. Increasing the size of urban green areas can reduce PM2.5; this effect was strongest for the southern city of Shanghai, may due to their different climates and green tree species. Energy consumption and emissions from primary industries were strongly positively correlated with the urban PM2.5 level. An in-depth understanding of the factors affecting PM2.5 concentrations could help policymakers improve air quality management strategies, especially for densely populated megacities.

Abstract. Brown carbon (BrC) is recognized as a considerable factor changing the atmospheric radiation balance. In addition to the biomass and biofuel sources, both field observations and laboratory studies suggest that fossil fuel combustion is an important contributor to BrC. This highlights a critical gap in the treatment of BrC in climate models, which typically categorize organic aerosols (OA) from fossil fuels as non-absorbing or simplistically assume that all OA are light-scattering. Here we present a regional simulation of BrC during a highly polluted winter in North China Plain (NCP) by using the WRF-Chem model incorporating currently known BrC sources with explicit absorption properties. The modified model generally performs well in simulating air pollutants and aerosols species against observations. Our simulations show that the average near-surface mass concentration of BrC in the NCP is 4.8 μg m-3 and its contribution to the aerosol absorption optical depth at 365 nm is 11.2 %. A diagnostic adjoint method has been used to quantify the overall direct radiative effect (DRE) of BrC and contributions from various sources. We find that the DRE of BrC is predominantly negative with an average of -0.10 W m-2 at the top of the atmosphere (TOA) over the NCP, and consequently decreases the direct radiative cooling effect of OA by 24.0 % with a TOA warming of up to +0.34 W m-2. Our findings reveal that residential coal combustion is the principal contributor to the DRE of BrC in the NCP, and a noteworthy contribution from secondary BrC.

Based on the data of the State of Global Air (2020), air quality deterioration in Thailand has caused ~32,000 premature deaths, while the World Health Organization evaluated that air pollutants can decrease the life expectancy in the country by two years. PM2.5 was collected at three air quality observatory sites in Chiang-Mai, Bangkok, and Phuket, Thailand, from July 2020 to June 2021. The concentrations of 25 elements (Na, Mg, Al, Si, S, Cl, K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Se, Br, Sr, Ba, and Pb) were quantitatively characterised using energy-dispersive X-ray fluorescence spectrometry. Potential adverse health impacts of some element exposures from inhaling PM2.5 were estimated by employing the hazard quotient and excess lifetime cancer risk. Higher cancer risks were detected in PM2.5 samples collected at the sampling site in Bangkok, indicating that vehicle exhaust adversely impacts human health. Principal component analysis suggests that traffic emissions, crustal inputs coupled with maritime aerosols, and construction dust were the three main potential sources of PM2.5. Artificial neural networks underlined agricultural waste burning and relative humidity as two major factors controlling the air quality of Thailand.

Atmospheric brown carbon (BrC) aerosols were investigated at two urban sites in southern (Hefei) and northern (Shijiazhuang) China during summer and winter of 2019-2020 to explore regional variability in their compositional and optical properties. Organic matter in ambient PM2.5 samples were characterized at molecular level using ultrahigh performance liquid chromatography coupled with a diode array detector and an Orbitrap mass spectrometer. Although the molecular composition of organic aerosols varied substantially over different ambient environments, they were mainly composed by CHO and CHON species in positive ionization mode while CHO and CHOS species in negative mode. The mass absorption coefficients of BrC aerosols at wavelength range 250-450 nm were relatively higher for winter samples in both cities and for Shijiazhuang samples in both seasons, partly attributed to the higher concentration levels of anthropogenic air pollutants in these environments. The absorption & Aring;ngstrom exponents further revealed that BrC aerosols in winter seasons and in Shijiazhuang had a greater capacity of absorption at shorter wavelengths. A total of 26 BrC species with strong absorption were unambiguously identified from different environments, which mainly consisted of CHO, CHON, and CHN species and had higher degrees of unsaturation and lower degrees of oxidation. The presence and abundance of these BrC species varied dynamically across the seasons and cities, with a greater number of speciespresented in the winter of Shijiazhuang. The BrC species together contributed 12-26 % in the total absorbance of light -absorbing organic components at 250-450 nm. This study highlights the regional differences in BrC properties influenced by the sources , atmospheric processes, which should be taken into account to assess their climate impacts.