Interactions between atmospheric chemical compounds and climate have a great impact on the earth system and atmospheric chemistry. However, the online two-way chemistry-climate coupled model, an indispensable tool for quantifying chemistry-climate interactions and projecting future air quality with climate change, remains sparse due to the considerable challenge in model complexity and computational resources. We present the development and evaluation of BCC-GEOS-Chem v2.0, which couples the GEOS-Chem chemical transport model (v14.0.1) with the Beijing Climate Centre Earth System Model (BCC-ESM). Based on the modular framework of BCC-GEOS-Chem v1.0, BCC-GEOS-Chem v2.0 further couples the Harmonized Emissions Component (HEMCO) to manage anthropogenic emission inventories and natural emissions, updates the chemical mechanism, includes the feedback of aerosols and greenhouse gases, and develops the capability for high-resolution simulation. The standard chemical mechanism in the BCC-GEOS-Chem v2.0 features a comprehensive Ox-NOx-VOC-halogen-aerosol chemical scheme for the troposphere and the stratosphere. We further evaluate the performance of the BCC-GEOS-Chem v2.0 simulation in representing atmospheric chemistry and compare with the model outputs from the BCC-GEOS-Chem v1.0 and BCC-AGCM-Chem over the simulated time period (2012-2014) at a spatial resolution of T42L26 (approximately 2.8 degrees & times;2.8 degrees and 26 vertical layers with a top at 2.914 hPa). BCC-GEOS-Chem v2.0 accurately depicts the primary seasonal and spatial distributions of tropospheric ozone observed by multiple instruments, showing small global mean biases of -2.1-1.8 ppbv for mid-tropospheric (700-400 hPa) ozone concentrations relative to satellite observations, along with a high spatial correlation coefficient (r) of 0.77-0.92 for individual seasons. It also demonstrates improved performance in simulating tropospheric carbon monoxide (CO), nitrogen dioxide (NO2), formaldehyde (CH2O) and surface PM2.5 compared to both BCC-GEOS-Chem v1.0 and the BCC-AGCM-Chem. The diagnostics of tropospheric ozone budgets (a global tropospheric ozone burden of 355 Tg) and OH concentrations (0.97 & times;106 molec.cm-3) are generally consistent with observation-constrained estimates and multi-model assessment. With the inclusions of aerosol-radiation and aerosol-cloud interactions, BCC-GEOS-Chem v2.0 reproduces the expected impacts of aerosols on radiative and cloud properties, e.g., decreasing shortwave downward solar radiation and outgoing longwave radiation, increasing cloud liquid water, and suppressing precipitation. The high-resolution simulation at T159L72 (approximately 0.75 degrees & times;0.75 degrees and 72 vertical layers with a top at 0.01 hPa) further improves the model capability in resolving the fine-scale plume transport dynamics and the pollution hotspot of NO2 and PM2.5, as well as the low ozone concentration in high-NOx environment in wintertime China. The development of the BCC-GEOS-Chem v2.0 model provides a powerful tool to study climate-chemistry interactions and for future projection of global atmospheric chemistry and regional air quality.

Reducing anthropogenic nitrous oxide emissions from agricultural lands is critical to combating climate change. Nitrification inhibitors and synthetic microbial consortia have already demonstrated potential in decreasing soil nitrification-derived nitrous oxide emissions. However, targeted interventions addressing denitrification sources while balancing agricultural sustainability remain challenging. Here, we identify triazole compounds as cytochrome P450 inhibitors capable of reducing soil denitrification-derived nitrous oxide emissions by 85-100% in laboratory settings. Extending our findings to in-situ paddy fields, we observe that triazoles (at 0.05% of fertilizer nitrogen) reduce nitrous oxide emissions by 33-54%. These potential inhibitors specifically target the NAD(P)H and nitric oxide binding sites of P450Nor, strongly inhibiting fungal nitrous oxide emissions. Occupancy of the heme-iron(III) site within the P450Nor active pocket enhances this selectivity, which is critical for effective inhibition. Given the projected increase in agricultural nitrous oxide emissions, our findings offer insights into the development of denitrification inhibitors and highlight triazole as a promising solution for nitrous oxide mitigation in agricultural ecosystems. A key gap in agricultural climate mitigation is the lack of interventions that specifically target denitrification. This study identifies that triazole compounds act as cytochrome P450 inhibitors, effectively reducing N₂O emissions from denitrification.

Circadian rhythms are mainly generated by a gene regulatory network (GRN) constituted by transcription factors (TFs). Comparisons between plant and mammalian circadian clock GRNs suggest conservation of the network architecture rather than its components. Therefore, a rhythm-generating capacity is not restricted to canonical circadian clock GRNs. Here, we showed that although the circadian clock GRN was arrhythmic, circadian rhythms were maintained in refrigerated postharvest strawberries. Through systematic dual-luciferase assays and network analysis, we discovered a noncanonical GRN pillared by five uncharacterized TFs. We developed a heterologous reconstitution system and demonstrated the rhythm-generating ability of this GRN. Subsequent systematic evolution of ligands by exponential enrichment followed by high-throughput sequencing (SELEX-seq), electrophoretic mobility shift assay (EMSA), and DNA affinity purification (DAP)-qPCR analyses suggested that this GRN was responsible for the circadian rhythms of downstream genes. Fruit-specific perturbation of this GRN led to enhanced susceptibility to Botrytis cinerea. Collectively, our study identified a noncanonical quasi-circadian GRN, realized the heterologous reconstitution of eukaryotic circadian GRNs, and demonstrated its function in immune regulation.

At present, there is currently a lack of unified standard methods for the determination of antimony content in groundwater in China. The precision and trueness of related detection technologies have not yet been systematically and quantitatively evaluated, which limits the effective implementation of environmental monitoring. In response to this key technical gap, this study aimed to establish a standardized method for determining antimony in groundwater using Hydride Generation-Atomic Fluorescence Spectrometry (HG-AFS). Ten laboratories participated in inter-laboratory collaborative tests, and the statistical analysis of the test data was carried out in strict accordance with the technical specifications of GB/T 6379.2-2004 and GB/T 6379.4-2006. The consistency and outliers of the data were tested by Mandel's h and k statistics, the Grubbs test and the Cochran test, and the outliers were removed to optimize the data, thereby significantly improving the reliability and accuracy. Based on the optimized data, parameters such as the repeatability limit (r), reproducibility limit (R), and method bias value (delta) were determined, and the trueness of the method was statistically evaluated. At the same time, precision-function relationships were established, and all results met the requirements. The results show that the lower the antimony content, the lower the repeatability limit (r) and reproducibility limit (R), indicating that the measurement error mainly originates from the detection limit of the method and instrument sensitivity. Therefore, improving the instrument sensitivity and reducing the detection limit are the keys to controlling the analytical error and improving precision. This study provides reliable data support and a solid technical foundation for the establishment and evaluation of standardized methods for the determination of antimony content in groundwater.

Climate change and environmental degradation caused by greenhouse gases (GHGs) and reactive nitrogen (Nr) emissions are getting exacerbated globally. As a major emitter of both GHGs and Nr, China faces double pressure of GHGs and Nr mitigation to achieve carbon neutrality and environmental sustainability. This study performed the first integrated analysis of the potential and the synergies of GHG (CO2, CH4, and N2O) and atmospheric Nr pollutant (NOx and NH3) mitigation based on multiple models. Here we show that with an integrated policy implementation, China can achieve a 66% reduction of GHG and 68% of air N pollutants by 2050, which would bring society benefits of 2,500 billion USD, 5 times exceeding the implementation costs. Synergistic emission reductions led by industry would be in advantage until around 2030 with carbon peak achieved, while agriculture-led reductions show improved synergies in abatement potential and cost-effectiveness after peak carbon. This demonstrates that the control priority on GHG and atmospheric Nr pollution needs to be switched in the post-peak period to achieve future zero carbon and clean air in China.

Surface ozone formation mechanisms differ between urban and nonurban areas because of distinct meteorological conditions and emission profiles, often leading to higher ozone concentrations in nonurban regions. Recent changes in climate and emissions have modified the ozone disparity between urban and nonurban areas in China; however, the temporal evolution of this difference remains debated, and its underlying causes are not yet fully understood. Here, using a high-spatiotemporal-resolution maximum daily 8-h average (MDA8) ozone data set for 2000-2024, we show that the ozone disparity in China first declined and then increased after 2013. Using machine learning approaches, including extreme gradient boosting (XGBoost) and Shapley additive explanations (SHAP), we identified the key factors influencing these changes across three major city clusters: Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD). From 2013 to 2019, reductions in anthropogenic nitrogen oxide emissions and fine particulate matter (PM2.5) concentrations were the primary contributors to the changing ozone disparity. Meteorological contribution to the variations in urban-nonurban ozone disparities increased after 2020. In the context of climate change, urban ozone mitigation in China faces escalating challenges, especially in the PRD, which is more susceptible to climatic shifts and exhibits more pronounced urban-nonurban ozone disparities.

Satellite observations play a crucial role in quantifying ammonia sources by capturing large-scale variations of atmospheric NH3 concentrations. As the world's first geostationary hyperspectral infrared sounder, the Geostationary Interferometric Infrared Sounder (GIIRS) on board China's FengYun-4 satellite series provides a unique opportunity to monitor the diurnal cycle of NH3. Using NH3 retrievals from July 2022 to June 2025, this study investigates the spatio-temporal variability of NH3 columns over East Asia, with a focus on daytime variations (07:00-19:00 LT - local time) in major agricultural regions. Inter-comparison with polar-orbiting IASI and CrIS data shows that GIIRS NH3 retrievals are consistent in capturing spatial patterns and temporal dynamics. The NH3 peaks occur between March and July, with peak timing earlier in the south and later in the north, reflecting regional differences primarily driven by agricultural activities. Validation with ground-based FTIR measurements at Hefei in eastern China demonstrates the accuracy of GIIRS NH3, with a correlation coefficient of 0.77 and an RMSE of 9.67 & times;1015 moleccm-2, while reproducing daytime variations observed by FTIR. For major agricultural areas, the NH3 columns generally increase from early morning to late afternoon, reaching 1.10-1.56 times morning levels in summer and spring. Compared with GEOS-CF model simulations, the results reveal pronounced discrepancies in spatial distributions over the Sichuan Basin in southwestern China and daytime variations over northern India. These findings highlight the valuable capability of FY-4B/GIIRS in identifying and tracking daytime dynamics of NH3 sources over East Asia, offering new insights beyond current low-Earth orbit (LEO) instruments.

Understanding how meteorology influences surface ozone variability is critical for interpreting trends and designing effective air quality policies. This study employs explainable machine learning (XML) with SHapley Additive exPlanations (SHAP) to interpret daily ozone variations from 2013 to 2023 across three major regions in eastern China: North China Plain (NCP), Yangtze River Delta (YRD), and Pearl River Delta (PRD). An ensemble of five machine learning models (LightGBM, XGBoost, CatBoost, Random Forest, and Extra Trees) is trained using 14 meteorological variables and two temporal indicators. XML reveals nonlinear, region-specific ozone-meteorology relationships that are broadly consistent with physical understanding, while differences in SHAP attributions across algorithms highlight structural uncertainty arising from multicollinearity among input variables. We use SHAP-derived contributions to attribute warm-season ozone trends to meteorological versus non-meteorological drivers. Before 2019, ozone increases are mainly associated with the temporal proxy for non-meteorological influences (e.g., emission changes), whereas after 2019 meteorological variability dominates regional ozone trends. Exploiting the additive nature of SHAP, we develop a de-weathering framework that partitions daily ozone into a SHAP-based climatological baseline and a meteorology-induced ozone anomaly (MOA). Across all three regions, the magnitude of positive MOA events increases over 2013-2023, while their frequency and duration show no significant trends, indicating that meteorological conditions increasingly amplify the intensity of ozone pollution episodes, without a corresponding increase in their frequency or duration. Our results demonstrate both the utility and limitations of XML for disentangling meteorological drivers of ozone pollution and provide new constraints on how meteorology shapes surface ozone under China's clean air actions.

Abstract. Accurate ammonia (NH3) emission inventories are critical for PM2.5 mitigation, yet bottom-up estimates remain uncertain, particularly for sector-resolved estimates in humid subtropical regions such as Guangdong, where urban-industrial emissions in the Pearl River Delta (PRD) coexist with dispersed agricultural sources in surrounding non-PRD areas. Here we integrate a ground-based NH3 network, Fengyun-4B (FY-4B) geostationary NH3 retrievals, a localized 3 km × 3 km prior inventory, and stretched-grid GEOS-Chem High Performance (GCHP) simulations at 0.2° × 0.2° resolution to inversely constrain monthly agricultural and non-agricultural NH3 emissions in the PRD and non-PRD Guangdong in 2023. The posterior simulation improved agreement with observations, reducing NRMSE from 55.3 % to 48.4 % and changing NMB from −9.0 % to 4.6 %, with further support from independent NH3, NH4+, and deposition measurements. Provincial anthropogenic NH3 emissions decreased from 477.6 to 441.5 kt yr−1: agricultural emissions declined from 437.2 to 362.5 kt yr−1, mainly through warm-season reductions in non-PRD areas, whereas non-agricultural emissions increased from 40.4 to 79.0 kt yr−1, especially during the cool season. Hypothetically removing agricultural (non-agricultural) NH3 emissions in Guangdong provided provincial PM2.5 reductions of 3.7 ± 1.5 (0.7 ± 0.4) μg m−3 and avoided premature deaths of 3251 (1064), highlighting the need to combine dispersed agricultural source controls and targeted non-agricultural source controls over populated PRD.

Ozone (O3) and fine particulate matter (PM2.5) are known to be interconnected due to shared precursor compounds. While numerous studies have examined the impact of precursors and meteorological factors on compound pollution events, few have proposed effective mitigation strategies tailored to specific regions. In this study, we conducted simulations of two types of O3 and PM2.5 pollution events in the Pearl River Delta (PRD) region during 2018 using the GEOS-Chem model. We applied a multiple linear regression model to quantify and distinguish the contributions of precursor emissions and meteorological factors to these events. Our findings highlight the predominant role of precursor emission factors in driving these pollution events. Notably, reducing NOx emissions in the Pearl River Estuary (PRE) region was found to exacerbate O3 pollution during specific periods, while reducing emissions of C4 alkanes (ALK4), lumped C3 alkenes (PRPE) and NH3 in proportion to their respective contributions emerged as an effective strategy to mitigate combined O3 and PM2.5 pollution. This research elucidates the mechanisms underlying O3 and PM2.5 compound pollution in the PRD region and presents a practical and significant approach to managing air pollution in this area.

China is a global hotspot for reactive nitrogen (Nr) emissions driven by its large livestock sector, which contribute to air pollution, climate change, and biodiversity losses. Despite their importance, current emission inventory development efforts often address singular Nr species, lacking a comprehensive presentation of all Nr species together and their interconnected features. This may jeopardize China’s achievements of carbon neutrality and clean air. In this study, we developed a high-resolution (0.1° × 0.1°) inventory of monthly Nr emissions from livestock manure in China for 23 livestock types from 2005 to 2022. Based on a unified dataset, our inventory provides detailed estimates of multiple Nr emissions from livestock, including ammonia, nitrogen oxides, and nitrous oxide. The inventory can serve as a valuable resource for atmospheric modelling and support integrated nitrogen management strategies in response to China’s evolving agricultural landscape, facilitating future decision-making to tackle environmental challenges associated with the agriculture sector.

Substantial forestation-induced greening has occurred over South China, affecting the terrestrial carbon storage and atmospheric chemistry. However, these effects have not been systematically quantified due to complex biosphere-atmosphere interactions. Here we integrate satellite observations, forestry statistics, and an improved atmospheric chemistry model to investigate the impacts of forestation on both carbon storage and ozone air quality. We find that forestation alleviates surface ozone via enhanced dry deposition and suppressed turbulence mixing, outweighing the effect of enhanced biogenic emissions. The 2005-2019 greening mitigated the growing season mean surface ozone by 1.4 ± 2.3 ppbv, alleviated vegetation exposure by 15%-41% (depending on ozone metrics) in forests over South China, and increased Chinese forest carbon storage by 1.8 (1.6-2.1) Pg C. Future forestation may enhance carbon storage by 4.3 (3.8-4.8) Pg C and mitigate surface ozone over South China by 1.4 ± 1.2 ppbv in 2050. Air quality management should consider such co-benefits as forestation becomes necessary for carbon neutrality. Forestation can aid both carbon neutrality and air quality. Here, the authors show that forestation in South China increases biomass carbon storage and improves surface ozone air quality by increasing dry deposition and reducing turbulence.

The climate impact of extreme boreal fires in 2021 and 2023 has drawn great attention for their record-high CO2 emissions. However, their climate impact extends beyond carbon. Fires also emit large amounts of reactive nitrogen, which plays a crucial role in the nitrogen and carbon cycles. Through top-down inversion of satellite observations, we estimate that the extreme boreal fires in 2021 and 2023 emitted 2.6 Tg N yr-1 and 4.9 Tg N yr-1 of NH3, respectively, which are comparable to agricultural-intensive regions, making boreal fires the second-largest contributor to the global reactive nitrogen budget. Unlike tropical fires, which emit more NOx than NH3, boreal fires are characterized by high NH3 emissions. With global warming likely to increase wildfire frequency, the rising NH3 emissions from boreal fires could have significant implications for the nitrogen and carbon cycles in that nitrogen-limited region, necessitating their consideration in future climate impact assessments.

In 2017, China launched a regulation on the compulsory coverage of construction wastes with dust-proof nets (DPNs) to mitigate air pollution resulting from urban construction activities. Challenges exist in quantifying how its implementation has mitigated airborne particulate matter (PM) pollution in China. Here we developed a framework combining high-resolution satellite images, machine learning, and an air quality model to identify DPNs and associated PM mitigation across China from 2016 to 2021. The total national DPN area surged significantly since 2017 and peaked in 2019, 9.78 times of that in 2016, especially in North China Plain (NCP). With a simultaneous increase of coverage duration, DPNs caused national PM emission reduction in 2019 was 23 times larger than 2016. Based on the extracted DPN dataset, we employed WRF-Chem regional air quality model to simulate the effects of DPNs on the PM-reduction across China. Next, we calculated that the use of DPNs helped a total of 253.8 million of people exposure to notably improved air quality and 26,836 people (95% CI: 23,630-30,041) avoided premature mortalities from 2017 to 2021. Accordingly, we quantified the economic benefits from DPNs use arcoss China reached 20.7 billion based on the Value of Statistical Life (VSL) model. Large potential remains for DPN use in China. The national DPN coverage could additionally increase by 3.3 times if DPN coverage rate in NCP would transfer to the entire country.

The Manaoke gold deposit is one of the most representative gold deposits in the Sichuan-Gansu-Shaanxi"Golden Triangle"area in the northwestern Yangtze Plate.Many published researches mainly focused on the basic geological features,fluid inclusions,and hydrogen-oxygen isotopes of the deposit.However,important issues regarding the source of ore-forming materials and the genesis of the Manaoke gold deposit remain controversial,due to the lack of systematic researches on the precise mineralogy and in-situ micron-sized sulfur isotopes of the Au-bearing pyrites.Thus,in this study,based on the petrographic observations,the in-situ sulfur isotopic analysis and elemental mapping/line scanning of the pyrites were conducted using the nanoscale secondary ion mass spectrometry(Nano-SIMS)method.Petrographic observations and backscattered images revealed that pyrites of ores in the deposit can be classified into the zoned pyrite and homogeneous pyrite.Especially,the zoned pyrite is characterized with the typical core-rim texture.The in-situ sulfur isotopic analytical results reflect that theδ34S values of the zoned pyrites are characterized with high value for the core but low value for the rim.Theδ34S values of the cores vary from+18.0‰ to+26.3‰,which are similar to that of the Triassic seawater,while those of the rims have a relatively large δ34S variation range(from-12.1‰ to+26.4‰),which are similar to those of the underlying sedimentary strata,indicating that the fluid for the formation of the rim of zoned pyrite was likely derived from the metamorphic dehydration of the strata.The homogeneous pyrites haveδ34S values varying from+17.0‰ to+24.5‰,which are similar to those of the cores of zoned pyrites.Meanwhile,the Nano-SIMS elemental mapping and line scanning results show that the core and rim of the zoned pyrite have obviously different characteristics of the elemental association and distribution.This means that comparing to the core of zoned pyrite,the rim of zoned pyrite is relatively enriched in Au,As,and Zn.Moreover,the rim of zoned pyrite is often composed of several even finer secondary zonations,proving that the formation of the rim is characterized with the periodicity and pulsation.Combined with the tectonic evolution history of the Western Qinling Orogen,the core of zoned pyrite of the Manaoke gold deposit was initially formed in the sedimentation and diagenetic stages.In the late Indosinian period,with the influence of regional metamorphism,the Early Paleozoic sedimentary strata underwent metamorphic dehydration to have produced the metamorphic fluids which carried Au released from the metamorphosed strata to some certain favorable structural positions to have formed the ore bodies through the enrichment and precipitatation of Au.

This paper constructs a mathematical model based on the analysis of Point of Interest (POI) information and real estate sales data, aiming to provide scientific guidance for the managers and decision-makers of City 1 and City 2, and assist them in achieving high-quality sustainable development under multiple challenges. Regarding the problems of housing price prediction and housing stock estimation, in the context of a downward trend in housing prices, five indicators such as the greening rate, the ratio of building area to parking space, and parking space management fees are constructed. The results show that there are no significant differences in the number of households, the number of parking spaces, and underground parking fees between the two cities.