Abstract:Beijing’s PM2.5 pollution control has reached an implementation bottleneck, demanding mechanistic understanding of nitrate (NO3?) as the predominant secondary pollutant whose diurnal formation discrepancy and its dominated factors affect refined management. Utilizing an ENVINT-DEN08 diffusion tube-filter membrane system, we conducted diurnal observations of PM2.5 and precursor gases (NH3, HNO3,) in July 2024, quantifying gas-particle partitioning (RG/P) and oxidation efficiency (ROxi) to elucidate diurnal formation of nitrate dynamics. Key findings reveal that nocturnal nitrate concentrations (6.2±6.8 μg/m3) doubled daytime levels (3.2±2.1 μg/m3), contrasting with gaseous HNO3 showing 2.4-fold higher daytime concentrations (4.5±2.3 vs. 1.9±0.7 μg/m3). Gas-to-particle conversion (CG/P=0.56) exhibited a higher contribution to nitrate formation compared to oxidation processes (COxi=0.44), and was identified as the primary driver of diurnal variations in particulate NO3? concentrations. The oxidation efficiency during daytime (ROxi=0.25) was significantly higher than that observed at nighttime. NO2 demonstrated no statistically significant impact on oxidation products (p>0.05).Gas-to-particle conversion (RG/P=0.42) was predominantly regulated by temperature, humidity, and aqueous liquid water content (ALWC) (p≤0.001). NH4NO3 displayed a pronounced dissociation tendency, with 95% P_{NH_3}\ast P_{HNO_3}/Kp<1 samples. Metal ions (Na++Ca2?+Mg2?+K?) exhibited significant involvement in NO3? formation (p≤0.01). The gas-particle conversion efficiency at night (RG/P=0.61) was higher than that during the day and was regulated by temperature and humidity, ALWC and pH (p≤0.01). The oxidation efficiency at night (ROxi =0.19) decreased; Sensitivity analysis revealed daytime NO3? exhibited heightened sensitivity to variations in total ammonia (TNH3= NH3+ NH4+) and metal ions. Under 90% control scenarios, NO3? concentrations decreased by 1.5 μg/m3 and 0.6 μg/m3 for TNH3 and metal ions, respectively. At night, NO3? was mainly affected by TNH3. When the control ratio of TNH3 was 90%, NO3? decreased by 5.1μg/m3.This study establishes a diurnal control framework to guide Beijing’s refined PM2.5 governance.

Abstract:Soybean is one of the world"s four major grain crops and the most important source of vegetable oil and protein. China, as the world"s fourth-largest soybean producer and largest consumer, relies heavily on Northeast China for domestic production, which accounts for approximately half of the national output. The interannual variability of soybean yield in Northeast China is primarily driven by meteorological factors. Its accurate prediction is crucial for food security and market stability. Previous statistical prediction studies have been limited to local areas or single provinces and have only provided fitting skills or short-term (≤5 years) prediction skills. To address these limitations, this study developed prediction models for interannual soybean yield variations at the provincial scale in Northeast China during 1981-2018 using six machine learning methods based on meteorological factors. The main findings are: (1) Ridge regression showed the best overall performance among the six methods, with cross-validation correlation coefficients reaching 0.48 (P<0.01), 0.58 (P<0.001), and 0.72 (P<0.001) in Heilongjiang, Jilin, and Liaoning provinces, respectively; (2) Compared to stepwise linear regression, ridge regression demonstrated superior performance in a correlation coefficient (R) and root mean square error (RMSE), with slightly lower accuracy only in amplitude prediction for Jilin and Liaoning provinces; (3) Predictor selection and sample augmentation generally improved the cross-validation prediction skills of machine learning models; (4) The critical meteorological impact window concentrated in the flowering and pod-setting period (July-August), during which the positive effects of temperature, precipitation, and sunshine duration significantly enhanced final yields through promoting pod formation, grain development, and photosynthesis. These findings provide scientific support for soybean yield prediction and agricultural risk management in Northeast China.

Abstract:The budget analysis of nitrous acid (HONO) is one of the challenging and hot topics in atmospheric chemistry research. This study conducted observations of HONO and related pollutants in urban Beijing during the spring of 2021. A simulation study was carried out to investigate the source and loss processes of high HONO events and their impact on O3 formation during the observation period. The results showed that the average concentration of HONO during the observation period was 2.55 ± 1.34 ppb, with two high HONO events (defined as the hourly maximum concentration of HONO exceeded 4 ppb for two consecutive days) accompanied by high PM2.5 and O3 concentrations. Based on the photochemical box model (F0AM) and observational data, the main sources and formation pathways of HONO were explored by coupling an updated heterogeneous chemical mechanism on the basis of a chemical mechanism (MCM3.3.1). The results indicated that the main daytime sources of HONO were photolysis of nitrates, the homogeneous reaction between NO and OH, and the photochemically enhanced heterogeneous reaction of NO2 on aerosol surfaces, with average formation rates (contribution ratios) of 2.70(55.8%), 0.53(10.8%), and 0.05(10.6%) ppb h-1, respectively. During nighttime, the contribution of heterogeneous reactions gradually increased, including the heterogeneous reactions of NO2 on the ground surfaces and aerosol surfaces, as well as heterogeneous reactions related to the enhanced aerosol surface uptake of NO2 with NH3, with average formation rates (contribution ratios) of 0.07(41.3%), 0.03(20.3%), and 0.03(20.0%) ppb h-1. The primary removal pathway for HONO during the day was photolysis, while dry deposition was dominant at night. Further simulation analyses revealed that the incorporation of the new HONO mechanism significantly enhanced both the formation and loss rates of ozone (O3). The sensitivity of O3 generation was observed to transition from a VOC-dominated regime to a synergistic control regime involving both VOCs and NOx. Elevated HONO concentrations and their source-sink processes were found to not only accelerate O3 production but also modify its generation sensitivity through dynamic regulation of O3 precursor concentrations (VOCs/NOx). Therefore, for the prevention and control of springtime ozone pollution in Beijing, it is crucial to correctly understand the feedback mechanism of HONO chemistry on ozone formation.

Abstract:Global warming has led to the intensification of the water cycle and the frequent occurrence of flash drought, which in turn has an important impact on the terrestrial ecosystem. However, the research on the response and recovery of vegetation to flash drought in Northeast China is relatively limited. This paper uses soil moisture which from ERA5-Land reanalysis data to select flash drought events and identify the summer flash drought in Northeast China from 2000 to 2022 firstly, and the spatial distribution characteristics during onset and recovery stages are analyzed. On this basis, the characteristics of different vegetation types response and recovery to flash drought are analyzed by combining with the MODIS Leaf Area Index (LAI), and the spatial pattern of dominant factors is further discussed by using random forest model and partial correlation analysis. The results show that there are significant differences in response and recovery of different vegetation types to flash drought in Northeast China. Specifically, forest has the longest response time (28 days) but the shortest recovery time (12 days), while grasslands has the shortest response time (10 days) but the longest recovery time (30 days). In addition, the decrease and recovery rate of forests are both faster (0.99/pentads and 1.02/pentads), while that of grasslands are slower (0.31/pentads and 0.41/pentads). The results of dominant factors suggest that soil moisture and Vapor Pressure Deficit (VPD) are the main factors affecting the response time of vegetation, the onset rate of flash drought and temperature play a major role in the decrease rate, the recovery time of vegetation is mainly impacted by soil moisture and precipitation, and the recovery rate is primarily influenced by soil moisture and VPD. Moreover, temperature and VPD determined the decrease and recovery rate of more than 50% of forests, respectively, and are higher than that of grasslands and croplands, while soil moisture dominates the response and recovery time of more than 73% of forests, grasslands and croplands. This paper results can provide some reference for the possible effects of flash drought on different ecosystems, and deepen the understanding of ecosystem response and recovery after flash drought.

Abstract:The atmospheric compound pollution events, primarily composed of fine particulate matter (PM2.5) and ozone (O3), represent one of the significant challenges facing China"s atmospheric environmental pollution. Accurate forecasting of atmospheric composite pollutants is essential for implementing effective pollution control and prevention measures. There is considerable uncertainty in forecasting the concentrations of compound pollutants. However, conventional observations are insufficient to meet the demands for their accurate forecasting. "Target observations" focus on the observational needs of forecasts and present a new observational strategy to enhance numerical forecasting skills. Currently, the target observation has been successfully applied in theoretical research and practical field trials for high-impact weather and climate events, significantly improving forecast skills. Compared to target observation research on high-impact weather and climate events, studies on target observation for air pollution events began relatively late and have not yet been implemented in field experiments. This paper reviews the research progress of target observation on high-impact weather and climate event forecasts, evaluates the application of targeted observation strategies in studies of severe air pollution events, and discusses the challenges currently faced. Additionally, it highlights key areas for future research and explores the critical role of targeted observations in improving the forecasting skills of atmospheric composite pollutants, aiming at providing scientific support for the precise management of atmospheric composite pollution.

Abstract:Lateral transport of DOC (Dissolved Organic Carbon) along the land-river-ocean continuum in terrestrial ecosystems is a key component of biogeochemical cycle. Quantifying the impacts of lateral transport of soil DOC on terrestrial carbon budget is of great significance for a deeper understanding of the global carbon cycle. This study conducted the simulations using the improved community land surface model version 5.0 (CLM5.0) toreveal the spatial and temporal characteristics of global GPP (Gross Primary Productivity) and soil DOC losses during the years 1981 to 2013, and investigated the impact of lateral transport of soil DOC on the terrestrial carbon budget. The results showed that the global soil DOC losses were increased significantly over the years with a multi-year average value of 458 Tg C yr-1. With the lateral transport of the soil DOC, the GPP and NPP (Net Primary Productivity) decreased in most regions of the world except for the northwestern part of South America and some regions of west-central Africa where the GPP and NPP were increased, which can be related to the lower increase in runoff flux and DOC reservoir compared to GPP and NPP. Overall, the global total GPP was reduced by about 8.61 Pg C yr-1 and NPP was reduced by about 7.28 Pg C yr-1 on a multi-year average basis due to the lateral transport of soil DOC. Moreover, the reduction of GPP has an increased trend over the years with an increase in soil DOC losses, while the reduction of NPP tended to be stable. The intra-annual reduction of GPP and NPP has an increased trend from May to July while has a decreased trend from July to November.

Abstract:To address the issue of insufficient consideration of the spatial heterogeneity of wind speed in existing methods for fog visibility prediction, which leads to low accuracy and stability, this paper constructs a Long Short-Term Memory Neural Network with Attention Mechanism (LSTM-AM) model for fog visibility prediction that takes into account the spatial heterogeneity of wind speed. The model quantifies the variation characteristics of wind speed at different spatial locations using a semi-variogram, integrating the spatial distribution of neighboring points and differences in wind speed. It employs wind direction angles and variation values to weight the features of wind speed spatial heterogeneity, effectively extracting these characteristics. Additionally, the Attention Mechanism (AM) enhances the LSTM method by improving its focus on key information, enabling the model to effectively capture and reflect the impact of critical meteorological factors on fog visibility. This enhances the model"s ability to pay attention to important temporal information and improves the accuracy of predictions under conditions of wind speed spatial heterogeneity. The results indicate that the proposed model improves R2 by 10%-20%, reduces RMSE by 25%-40%, and decreases MAE by 26.3%-39.1%, demonstrating high accuracy and stability in fog visibility prediction.

Abstract:The period of May to June marks a critical transition from the dry season to the rainy season in Yunnan, where the amount of precipitation during this time significantly impacts local agricultural production and the ecological environment. Furthermore, it serves as an important indicator of the early or late onset of the rainy season. This study examines the variability characteristics of precipitation in Yunnan during May to June, utilizing observational precipitation data from 1971 to 2022 along with concurrent NCEP/NCAR reanalysis data. Our analysis reveals a significant decreasing trend in the cumulative precipitation in southwest Yunnan from May to June, with a climate change point occurring in 2009, followed by a marked reduction after 2010. Further investigation indicates that the warm anomaly sea surface temperature (SST) in the tropical western Indian Ocean weakens the divergence at 200hPa and convergence at 700hPa over the northern Indo-China Peninsula and Yunnan, hindering vertical atmospheric uplift and precipitation. Additionally, it causes a positive anomaly in the 500hPa height field over Central Asia and the Bay of Bengal, weakening the cold air from mid-to-high latitudes and the southwest airflow ahead of the southern branch trough, thereby inhibiting the formation of precipitation weather processes in southwest Yunnan. Concurrently, an anomalous anticyclonic circulation emerges from the South China Sea to the Indo-China Peninsula in the lower atmosphere, resulting in anomalous northwest winds in Yunnan and a weakened moisture transport from the southwest winds, further contributing to the decrease in precipitation in southwest Yunnan from May to June. The continuous increase in the SST of the tropical western Indian Ocean and its abrupt warming in the late 2010s are identified as the key drivers for the abrupt decrease in precipitation in southwest Yunnan.

Abstract:The observational data of meteorological stations during 1980～2019 in China was used in this study, including daily precipitation, daily maximum temperature, humidity, cloud cover and wind speed. Then holiday climate index (HCI) is constructed by these meteorological elements to show holiday climatic suitability over China. The annual mean holiday comfort day (HCD) is 131.1 days over China, with a bimodal distribution throughout the year, which is more in spring and autumn while less in winter and summer. Affected by the effective temperature and wind speed, the annual mean HCD show a significant increasing trend. The bimodal distribution of HCD is more prominent caused by effective temperature, cloud cover and precipitation. The spatial distribution of annual mean HCD is not even in China, which is greater in North China, Huanghuai, Jianghuai, Xinjiang and Yunnan province than those in other regions. HCD in Tibetan is the least in China. HCD in most areas over China has an increasing trend, except for central South China and eastern Hebei province. The spatial distributions of HCD in winter, spring and autumn show increasing trends in most part of China, while decreasing trends in summer. Closely attention on the influence of climate change on holiday comfort over China is conducive to the rational development and utilization of climate resources, providing protection for the sustainable development of tourism industry.

Abstract:Based on datasets of three drought indices (i.e., the Standardized Precipitation Index, SPI-12, Standardized Precipitation Evapotranspiration Index, SPEI-12, and self-calibrated Palmer Drought Severity Index, scPDSI) for the period 1901~2020, this study investigates the long-term characteristics of drought in China over the past 120 years and then explores reasons for their inconsistency. Results indicate a significant drying trend in southwestern China, the Loess Plateau, southern Northeast China, and southern Xinjiang, while regions with significant wetting trends are located in North China, the east part of Northwest China, and the north part of Northeast. As far as drought events are concerned, both Northeast China and Northwest China are characterized by prolonged duration and higher intensity, but the drought tends to decrease in general, especially in eastern Northwest China and northern Northeast China. On the contrary, the duration and intensity of drought events increased in southern Northeast China. There are approximately 2820000km2 of land area each year that has experienced drought, and among them about 30500 km2 of land areas are threatened by extreme drought. Drought severity increases with time, with rapid growth after the mid-1990s. During the past 120 years, drought area exhibits significant interannual and decadal variabilities, with the main periods of 2~3 years and 18~22 years, respectively. In humid regions, three drought indices show good consistency, while in semi-arid and arid regions, their consistency is relatively low. In humid areas, the wet-dry variation is dominated by precipitation. In semi-arid and arid areas, besides precipitation, both temperature anomaly and soil characteristics also play an important role in drought. Therefore, more attention should be paid to the drought index selection over arid and semi-arid areas in the context of global warming.