This study investigates the influence of complex terrain on air pollution dispersion during a valley-basin pollution episode in Lanzhou, northwestern China, using the WRF-CALPUFF modeling system and terrain-modified sensitivity experiments to quantify these topographic effects and reveal their underlying mechanisms. Results indicate that orographic features increase NO2 concentrations in urban areas by approximately 30 % on average, while channeling effects enhance ventilation and reduce pollutants in northwestern valleys. Analysis of atmospheric dynamics and thermodynamics reveals that thermally-induced local circulations combined with topographic stagnation create recirculation zones that effectively trap pollutants, as evidenced by recirculation factor analysis showing pronounced accumulation in these zones compared to well-ventilated areas. Valley heat deficit (VHD) demonstrates a strong correlation with NO2 levels (R = 0.77), where elevated VHD coincides spatially with recirculation zones and pollution hotspots. The study identifies a critical VHD threshold of 1.4 MJ/m2, beyond which pollution episodes occur due to suppressed vertical mixing from enhanced atmospheric stability and restricted horizontal transport from terrain-induced flow blockage. These findings highlight how the combined effects of thermal stabilization and flow obstruction work synergistically to sustain pollution accumulation in mountainous regions. The research establishes a comprehensive framework that elucidates the coupled dynamic-thermodynamic mechanisms through which terrain characteristics modulate pollution dispersion patterns, offering valuable insights into boundary layer processes and airflow dynamics during pollution events, with important implications for understanding urban air quality deterioration in complex terrain environments.

Increased atmospheric nitrogen (N) deposition alters the structure and function of soil microbial communities in terrestrial ecosystems, consequently exerting a profound influence on ecosystem processes. However, the effects of N deposition on soil microbial network complexity and its regulation of soil carbon (C) processes in semiarid grassland ecosystems are poorly understood. In this study, based on a 13-year multilevel field N addition experiment in a semiarid grassland on the Loess Plateau, together with metagenomic sequencing and cooccurrence network analysis methods, we observed that the complexity of microbial co-occurrence network, characterized by the number of nodes and edges and the average path length, increased first and then decreased in a nonlinear response to N addition, with thresholds between 4.60 g N m- 2 yr- 1 and 9.20 g N m- 2 yr- 1 in both the topsoil and subsoil. Meanwhile, soil microbial network complexity was significantly positively correlated with plant root traits (e.g., root biomass), soil microbial properties (e.g., fungal community composition and bacterial Shannon diversity and community composition) and most physicochemical properties (e.g., soil water content, NH4+-N, and Fep). Structural equation model analysis (SEM) revealed that the major determinants of the soil microbial network complexity shifted from soil physicochemical properties to bacterial community composition along the N addition gradient. Further analysis revealed that N-induced alterations in microbial network complexity could modulate soil organic C (SOC) formation, preservation, and decomposition by affecting the functional potential of microbial communities. For instance, the microbial network complexity, abundance of functional genes involved in starch and hemicellulose degradation, and microbial C use efficiency decreased significantly under high levels of N addition. These results provide empirical evidence for the close linkages between soil microbial network complexity and soil C processes and highlight the need to disentangle the mechanisms underlying the nonlinear response of soil microbial interactions to atmospheric N deposition to improve soil C projections.

Nitrogen (N) fertilization is known to impact the capacity of ecosystems to support multiple ecosystem services such as carbon sequestration and nutrient cycling, particularly in nutrient‐limited environments. Yet, little is known about how N fertilization may result in trade‐offs across contrasting soil ecosystem services. Moreover, the contribution of soil microbial networks as mediators of the impacts of fertilization on soil ecosystem services is poorly understood. Here we collected topsoil (0–10 cm) and subsoil (10–20 cm) samples from a 13‐year N addition experiment in a semiarid grassland to investigate how long‐term N additions affect soil multiservices. We found that soil multiservice predominantly exhibited a hump‐shaped response to the increasing levels of N addition across two soil depths. More importantly, changes in the complexity of soil microbial networks were positively correlated with ecosystem multiservices across the two soil depths. This relationship was especially important in explaining topsoil multiservice responses, while in subsoils, multiservices were more strongly associated with abiotic properties than network complexity. This distinction may be attributed to the lower microbial activity and reduced nutrient utilization capacity in subsoils, which allows abiotic factors to play a more dominant role on multiservices. Synthesis . Our results highlight that soil microbial network complexity is highly correlated with multiple ecosystem services in the context of global atmospheric N deposition.

Alpine grassland degradation is a major threat to global carbon cycles, yet the microbial mechanisms driving soil organic carbon (SOC) loss remain poorly understood. Ecological stoichiometry theory provides a framework for understanding how resource imbalances constrain microbial activity and metabolism. Here, we investigated how grassland degradation altered the stoichiometric imbalances between soil microbes and their resources and how microbes coped with such imbalances, as well as the implications of their responses for SOC stock. We established a degradation gradient (non-, light, moderate, and heavy) in both an alpine meadow and an alpine steppe on the Qinghai-Tibet Plateau, China, with analyzing vegetation nutrient storage, soil physicochemical properties, microbial biomass, dissolved organic nutrients, extracellular enzyme activities, and nutrient mineralization rates. Our results showed that C:N stoichiometric imbalance exhibited a hump-shaped response to grassland degradation with a maximum around moderate degradation, while C:P and N:P stoichiometric imbalances significantly decreased with increasing grassland degradation levels in both ecosystems. However, microbial responses were ecosystem-specific: meadow microbes showed strong C:N:P homeostasis, while steppe microbes showed weaker C:N and C:P homeostasis, indicating higher stoichiometric plasticity. Mechanistically, microbes coped with these shifting imbalances by adjusting extracellular enzyme stoichiometry, net N mineralization, and soil microbial respiration. For instance, C:P and N:P stoichiometric imbalances were strongly linked to the relative production of P-acquiring enzymes across both ecosystems, with slightly stronger correlations in meadows. These response mechanisms were significantly correlated with SOC stock, suggesting that microbial metabolic adjustments are a key pathway regulating the 14.8–71.5% decline in SOC stock decline observed during degradation. Our findings provide a mechanistic link between grassland degradation, microbial stoichiometric response, and carbon cycling, highlighting that ecosystem-specific microbial strategies are critical determinants of SOC vulnerability in these sensitive high-altitude ecosystems.

Soil net nitrogen mineralization (Nmin), a microbial-mediated conversion of organic to inorganic N, is critical for grassland productivity and biogeochemical cycling. Enhanced atmospheric N deposition has been shown to substantially increase both plant and soil N content, leading to a major change in Nmin. However, the mechanisms underlying microbial properties, particularly microbial functional genes, which drive the response of Nmin to elevated N deposition are still being discussed. Besides, it is still uncertain whether the relative importance of plant carbon (C) input, microbial properties, and mineral protection in regulating Nmin under continuous N addition would vary with the soil depth. Here, based on a 13-year multi-level field N addition experiment conducted in a typical grassland on the Loess Plateau, we elucidated how N-induced changes in plant C input, soil physicochemical properties, mineral properties, soil microbial community, and the soil Nmin rate (Rmin)-related functional genes drove the responses of Rmin to N addition in the topsoil and subsoil. The results showed that Rmin increased significantly in both topsoil and subsoil with increasing rates of N addition. Such a response was mainly dominated by the rate of soil nitrification. Structural equation modeling (SEM) revealed that a combination of microbial properties (functional genes and diversity) and mineral properties regulated the response of Rmin to N addition at both soil depths, thus leading to changes in the soil N availability. More importantly, the regulatory impacts of microbial and mineral properties on Rmin were depth-dependent: the influences of microbial properties weakened with soil depth, whereas the effects of mineral protection enhanced with soil depth. Collectively, these results highlight the need to incorporate the effects of differential microbial and mineral properties on Rmin at different soil depths into the Earth system models to better predict soil N cycling under further scenarios of N deposition.

Plantations make up a sizable component of forest ecosystems in China's drylands and play an important role controlling the energy balance and water cycle. However, there is limited knowledge regarding the exchange of water and energy in dryland plantations, particularly those located within the intricate and unique environments of dryland mountain regions. The focus of this study was the investigation of a Larix principis-rupprechtii plantation within a dryland mountain ecosystem located in Northwest China. The eddy covariance method was employed to calculate the water and energy fluxes, while the thermal dissipation probe (TDP) method was utilized to measure forest overstory transpiration (T) from 2018 to 2021. The links between energy allocation and various components in water flux were investigated, as well as the variable dynamics of water and energy flux and their relationship with environmental conditions. The findings demonstrated that the annual mean cumulative evapotranspiration (ET) was about 510 mm, accounting for over 82.8 % of the precipitation, which was mainly from soil evaporation and understory transpiration, rather than overstory transpiration (T). The average value of T/ET was 0.25 for the growing season, and surface conductance (Gs) was always higher than canopy conductance (Gc) on both daily and monthly scales. The evaporative fraction (EF) and Priestley-Taylor model coefficient (alpha) in the growing season were significantly higher than those in the non-growing season, and alpha>1 from June to September, indicating that the ecosystem in the study area had sufficient water supply. Furthermore, during the growing season, the ratio of latent heat flux (LE) to net radiation (Rn) was larger than that in the non-growing season, while the proportion of sensible heat flux (H) was opposite, and soil heat flux (G) accounted for the smallest proportion of net radiation. This study provides valuable insights into the energy fluxes and water fluxes within the ecosystem of dryland mountain plantations, and it serves as a guide for choosing afforestation tree species in the future.

Soil microbes are subject to stoichiometric imbalances, which are the dissimilarities in elemental stoichiometry between microbial biomass and resources. Shifts in dominant plant species co-occur with unparallel changes in the stoichiometry of soil microbial biomass and resources, leading to stoichiometric imbalances. However, how soil microbes deal with stoichiometric imbalances induced by changes in dominant plant species, and what the implications are for soil carbon cycling, remain unknown. Here, we compared the stoichiometric imbalances of five plant patch types with the dominant plant community (Kobresia pygmaea) in a Tibetan alpine grassland to examine how soil microbes respond physiologically to stoichiometric imbalances, thereby affecting soil microbial respiration (SMR). We found that C:N and C:P imbalances varied between plant patches and differed significantly from those in the soil of K. pygmaea. We also found that the regulation of extracellular enzyme production, SMR, potential microbial carbon use efficiency (CUE), net N mineralization, and net ammonification were essential mechanisms for soil microbes to deal with C:N imbalances. Simultaneously, soil microbes dealt with fluctuating C:P imbalances by regulating their net P mineralization and CUE. Further, structural equation modeling revealed that stoichiometric imbalances induced by changes in dominant plant species could indirectly affect SMR by regulating extracellular enzyme stoichiometry and net nutrient mineralization. These results highlight the importance of the stoichiometry of soil microbe/resource interactions in regulating metabolic activities and modifying terrestrial carbon flows in shifting plant communities.

Soil net nitrogen mineralization (Nmin), a microbial-mediated conversion of organic to inorganic N, is critical for grassland productivity and biogeochemical cycling. Enhanced atmospheric N deposition has been shown to substantially increase both plant and soil N content, leading to a major change in Nmin. However, the mechanisms underlying microbial properties, particularly microbial functional genes, which drive the response of Nmin to elevated N deposition are still being discussed. Besides, it is still uncertain whether the relative importance of plant carbon (C) input, microbial properties, and mineral protection in Nmin regulating under continuous N addition would vary with the soil depth. Here, based on a 13-year multi-level field N addition experiment conducted in a typical grassland on the Loess Plateau, we elucidated how N-induced changes in plant C input, soil physicochemical properties, mineral properties, soil microbial community, and soil net N mineralization (Rmin)-related functional genes drove the responses of Rmin to N addition in the topsoil and subsoil. The results showed that Rmin increased significantly in both topsoil and subsoil with increasing rates of N addition. Such a response was mainly dominated by the rate of soil nitrification. Structural equation modeling (SEM) revealed that a combination of microbial properties (functional genes and diversity) and mineral properties regulated the response of Rmin to N addition at both soil depths, thus leading to changes in the soil N availability. More importantly, the regulatory impacts of microbial and mineral properties on Rmin were depth-dependent: the influences of microbial properties weakened with soil depth, whereas the effects of mineral protection enhanced with soil depth. Collectively, these results highlight the need to incorporate the effects of differential microbial and mineral properties on Rmin at different soil depths into the Earth system models to better project soil N cycling under further scenarios of N deposition.

Realistic representation of land-atmosphere processes (LAPs) plays a significant role in meteorology and consequent air quality simulations. In this study, 15 series setups, different combinations of five planetary boundary layers (PBL) with compatible surface layer (SL) schemes, and three land surface models (LSMs) were designed in the WRF-Chem model to perform a sensitivity study assessing the influence of LAPs on simulations. Sensitivity tests were conducted over the river valley city at 1 km resolution under synoptically quiescent conditions on December 1-6, 2019. The results were evaluated via comparison with five meteorological and 29 air quality stations. Statistical indicators were used to understand the physical mechanisms of various LAPs which impact air pollution diffusion and to identify the preferred configuration. Our studies revealed that, under synoptically quiescent conditions over complex topography, adequate representations of physical processes occurred in lower atmosphere have critical implications on numerical simulation of regional meteorology and air quality. The simulations seemed more sensitive to the LSMs than to PBL schemes. The NoahMP land surface model exhibited better performance than the RUC and SLAB models, likely because it effectively captured the daily variations in near-surface temperature and wind direction. LAPs had a significant effect on the air quality simulations. The differences in correlation coefficients and daily particulate matter (PM) concentrations between the model simulations in response to the LAP configurations reached 86%-94% and 42%-48% respectively. The MYN_N configuration, namely the NoahMP land surface model coupled with the MYNN PBL scheme, improved the accuracy of the modeled PM concentration and constituted a reference for air quality prediction over complex topography under calm synoptic conditions.

通过部门调研、现场调查和遥感解译等方法获取天水市主城区大气污染源活动水平数据,采用排放因子法估算了天水市主城区 10类污染源的9种污染物排放量,构建了2019年天水市主城区高分辨率排放清单,并采用横向比较法和模式验证法评估了排放清单的合理性.结果表明:(1)2019年天水市主城区 SO2、NOx、CO、VOCs、NH3、PM10、PM2.5、BC 和OC 的排放量分别为2702,8829,82670,10460,7551,14221,8252,1682和2814t.化石燃料固定燃烧源为SO2、CO和颗粒物的主要贡献源,移动源是NOx和VOCs的主要贡献源,NH3排放主要来源于农业源.(2)天水市主城区SO2、NOx、颗粒物、CO和VOCs的排放高值区主要集中在人口和工业密集的河谷地形内,NH3排放高值区分布在周边耕地区域.(3)民用散烧源的空间分配方案对模拟结果有重要影响,在河谷地形集中供热范围外,根据城中村的分布对民用散烧源进行空间分配较为合理.(4)WRF-Chem模式模拟的4,7,10和12月的SO2、NO2、O3、CO、PM10和PM2.5日均浓度与同期监测的污染物浓度相关系数分别为0.767,0.502,0.618,0.462,0.647和0.654.

Aims Grasslands are experiencing severe degradation globally, impacting aboveground vegetation and soil properties. The influences of grassland degradation on bacterial communities in soil are not well-understood. Methods The normalized difference vegetation index (NDVI) was calculated to represent grassland status and indicate grassland degradation (decreasing NDVI). Soil ph, bacterial communities, as well as nutrient and organic carbon concentrations were measured. Results Bacterial alpha diversity had negative relationships with soil moisture, soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP). Bacterial community structure was significantly associated with NDVI, the change rate of NDVI, moisture, ph, SOC, TN, as well as soil C:N and C:P ratios. Bacterial phyla were differentially related with these environmental variables. Moreover, network analysis showed that the network of soil bacteria had strong cooperation relationships (positive correlations between taxa) and was grouped into three modules. According to modularity, 71 keystone taxa were detected as network connectors and module hubs. All the modules had close relationships with environmental variables, and many keystone taxa were negatively associated with soil moisture and SOC. Conclusions These results suggest that grassland degradation might be responsible for shifts in soil bacterial communities in terms of alpha diversity and community structure through changes in soil moisture, SOC, nutrients, and C:nutrient ratios. Moreover, in the network analyses, the strong co-occurrence relationships between taxa as well as close relationships between environmental variables and module structures and keystone taxa suggest low stability and high vulnerability of bacterial communities to the influences of grassland degradation.

基于甘肃省2018～2019年颗粒物质量浓度监测数据,分析了全省大气颗粒物浓度的时空变化及排放特征,并利用HYSPLIT后向轨迹模式研究了颗粒物传输路径.结果 表明:颗粒物(PM10和PM2.5)空间分布呈现区域特征:PM10浓度高值位于河西走廊地区,由北向南呈阶梯式递减;PM2.5以陇中地区为高值中心,向南北两侧递减,陇南地区为全省颗粒物清洁区.不同地区PM10与PM2.5地面浓度季节变化特征存在差异,陇中、陇东和陇南地区PM10和PM2.5浓度变化特征一致,陇中和陇东地区颗粒物(PM10与PM2.5)浓度冬高夏低,陇南地区则为冬高秋低;河西走廊PM10和PM2.5浓度季节变化不同,PM2.5冬高夏低,PM10春高夏低.后向轨迹聚类结果表明全省春季、冬季均受到来自中亚及新疆的偏西气流影响,该路径输送下可吸入颗粒物(PM10)浓度明显高于其他路径,是典型的沙尘输送路径,4大分区受沙尘传输影响程度依次为河西＞陇中＞陇东＞陇南,来自陕西、川渝的偏东路径是陇南地区颗粒物的主要输送路径,该路径下PM2.5/PM10比值大于0.5,明显高于偏西路径,说明偏东路径人为源污染贡献显著.研究结果有助于全面认识全省颗粒物污染特点、为分区制定颗粒物污染防治政策、以及区域污染协同治理提供科学的参考依据.

颗粒物浓度的数值模拟能够反映颗粒物的空间分布特征,对于防治大气颗粒物污染具有一定意义.利用MEIC清单和第二次全国污染源普查(简称"二污普")数据统计的甘肃省工业源、电力源、农业源、民用源和交通源五类源的主要污染物排放量,分析了污染源排放的空间分布特征,利用WRF-Chem模式模拟了甘肃省2019年1月PM10和PM2.5浓度,将模拟结果与甘肃省33个环境空气质量国控监测点颗粒物日均监测数据进行对比,检验WRF-Chem模式模拟的性能,进一步分析了甘肃省颗粒物浓度的空间分布特征.结果表明:①甘肃省SO2、NOx、PM10,PM2.5、VOCs、NH3和CO在1月的排放量分别为2.12×104、2.96×104、2.97×104、2.43×104、3.18×104、1.27×104和3.04×105 t,除NH3外,其他污染物排放高值主要分布在兰州市、嘉峪关市等工业发达地区.②33个环境空气质量国控监测点模拟与监测的PM10和PM2.5浓度的相关系数分别为0.544和0.597,颗粒物的模拟值与监测值有较好的相关性;WRF-Chem模式模拟结果显示,PM10和PM2.5浓度高值分布在兰州市,次高值分布在天水市和庆阳市,甘南藏族自治州以及河西地区颗粒物浓度较低,这是甘肃省工业布局、扩散条件和地形条件综合作用的结果.研究显示,WRF-Chem模式可以较好地模拟甘肃省区域颗粒物浓度时空分布特征.

污染气象成因和污染物区域传输作用对本地污染影响较大,研究不同地区污染气象成因和污染物区域传输作用对本地污染的治理有重要意义.利用污染物浓度监测和气象要素观测资料,采用统计学分析方法、特征雷达图和HYSPLIT-4后向轨迹模型分析宝鸡市2018年12月29日—2019年1月8日一次持续性重污染过程的气象成因和污染特征.结果 表明:①此次重度污染持续时间长、强度大,污染过程中有6 d空气质量指数(AQI)达到重度及以上污染(AQI>200),ρ(PM2.5)平均值达205.4μg∕m3,有2 d达到严重污染(AQI>300).②气象条件对污染物浓度的影响显著,高低空环流形势形成稳定层结,容易造成污染物累积.东南大风将污染气团远距离输送到宝鸡市,西北静小风使得污染物在本地聚集加重污染.③重污染维持阶段,ρ(PM2.5)∕ρ(PM10)在0.9左右,说明此次污染过程PM2.5占比较大,污染物的二次转化作用明显;ρ(NO2)∕ρ(SO2)为6.2,表征移动源贡献率高于固定源;ρ(CO)∕ρ(SO2)呈先增后减的变化特征,表明静稳天气持续,本地源排放对重污染的贡献逐渐凸显.④特征雷达图结果表明,此次重污染过程的污染类型由发展阶段的污染特征不明显和燃煤型污染特征,逐渐转化为偏二次污染类型,重污染过程结束后污染类型以偏扬尘型为主.研究显示,气象条件和传输扩散对宝鸡市重污染影响显著,宝鸡市重污染应急需优先管控移动源,汾渭平原应加强区域联动,共同治理环境污染问题.

Grasslands across the world are being degraded due to the impacts of overgrazing and climate change. However, the influences of grassland degradation on carbon (C), nitrogen (N), and phosphorus (P) dynamics and stoichiometry in soil ecosystems are not well studied, especially at high elevations where ongoing climate change is most pronounced. Ecological stoichiometry facilitates understanding the biogeochemical cycles of multiple elements by studying their balance in ecological systems. This study sought to assess the responses of these soil elements to grassland degradation in the Qinghai Lake watershed on the Qinghai-Tibet Plateau (QTP), which has an average elevation of >4000 m and is experiencing serious grassland degradation due to its sensitivity and vulnerability to external disturbances. Substituting space for time, we quantified normalized difference vegetation index to gauge grassland degradation. C, N, and P concentrations and their molar ratios in soil and in soil microbial biomass were also measured. The results showed that grassland degradation decreased the concentrations of C and N, as well as the ratios of C:P and N:P in soil. The soil became relatively more P rich and thus N limitation is anticipated to be more apparent with grassland degradation. Moreover, C, N, and P concentrations in soil microbial biomass decreased with increased grassland degradation. C:N:P ratios of soil microbial biomass were highly constrained, suggesting that soil microorganisms exhibited a strong homeostatic behavior, while the variations of microbial biomass C:N:P ratios suggest changes in microbial activities and community structure. Overall, our study revealed that grassland degradation differentially affects soil C, N, and P, leading to decreased C:N and N:P in soil, as well as decreased C, N, and P concentrations in soil microbial biomass. This study provides insights from a stoichiometric perspective into microbial and biogeochemical responses of grassland ecosystems as they undergo degradation on the QTP.

Grassland is among the largest terrestrial biomes and is experiencing serious degradation, especially on the Qinghai–Tibet Plateau (QTP). However, the influences of grassland degradation on microbial communities in stream biofilms are largely unknown. Using 16S rRNA gene sequencing, we investigated the bacterial communities in stream biofilms in sub-basins with different grassland status in the Qinghai Lake watershed. Grassland status in the sub-basins was quantified using the normalized difference vegetation index (NDVI). Proteobacteria, Bacteroidetes, Cyanobacteria, and Verrucomicrobia were the dominant bacterial phyla. OTUs, 7,050, were detected in total, within which 19 were abundant taxa, and 6,922 were rare taxa. Chao 1, the number of observed OTUs, and phylogenetic diversity had positive correlations with carbon (C), nitrogen (N), and/or phosphorus (P) in biofilms per se. The variation of bacterial communities in stream biofilms was closely associated with the rate of change in NDVI, pH, conductivity, as well as C, N, P, contents and C:N ratio of the biofilms. Abundant subcommunities were more influenced by environmental variables relative to the whole community and to rare subcommunities. These results suggest that the history of grassland degradation (indicated as the rate of change in NDVI) influences bacterial communities in stream biofilms. Moreover, the bacterial community network showed high modularity with five major modules (>50 nodes) that responded differently to environmental variables. According to the module structure, only one module connector and 12 module hubs were identified, suggesting high fragmentation of the network and considerable independence of the modules. Most of the keystone taxa were rare taxa, consistent with fragmentation of the network and with adverse consequences for bacterial community integrity and function in the biofilms. By documenting the properties of bacterial communities in stream biofilms in a degrading grassland watershed, our study adds to our knowledge of the potential influences of grassland degradation on aquatic ecosystems.

Particulate matter (PM) concentrations are affected by anthropogenic emissions and sand transport jointly; however, the relative contributions from those two aspects are usually unknown. In our work, statistical analysis and back trajectories model were used to identify the dominant source in such area, by taking Yumen City as an example. We come to the conclusion that local emissions dominate the concentration of airborne pollutants, while sand transport plays a significant role on PM concentration. The conclusions were supported by the following results. (1) PM monthly mean concentrations at the two air quality stations, which are 70 km far away from each other, have the similar levels and variation trend; furthermore, a regression analysis of PM2.5 and PM10 daily concentrations between both stations indicated a significant correlation, suggesting that PM at both locations was influenced by the same emission sources; (2) statistical analysis results revealed that PM concentration has a positive correlation with wind speed, indicating the wind-blown dust and sand contribute mainly on PM concentration; (3) back-trajectory clustering analysis indicates that long-distance transport particulates from dust sources and their pathways had a significant impact on local PM concentrations.

利用中尺度天气预报数值模式(WRF)中的3种边界层参数化方案(YSU、MYJ和ACM2)模拟了重庆2012年8月30日-9月1日的一次暴雨过程,结合同期的观测资料分析了3种不同边界层参数化方案模拟降水强度和降水落区的差异,对各方案模拟重庆区域内的降水能力进行了检验评估.通过垂直速度、水汽通量散度和相对涡度等基本物理量的诊断,对比分析了不同方案模拟降水的差异.结果表明,3种边界层方案均能模拟出此次雨带的移动方向,但YSU方案能较好地模拟出降水落区、降水强度和降水趋势,MYJ方案模拟效果次之,ACM2方案模拟效果较差.YSU方案模拟降水的动力条件和水汽条件配置符合实际降水情况,是模拟效果较好的主要原因.