地形起伏地区管道水联运充水排气过程分析

针对油气长输管道投产前通常会进行充水清管、调试和吹扫(常规称:水联运),由于埋地管道随地形起伏经过无数个相对高点和低点,管道充水前处于自然状态,一旦管内充水,就会将管内空气归聚成封闭空间,形成多个带压气团随水头移动,时而被压缩,时而被膨胀,从而造成管道断流,影响泵站正常工作,严重的还会导致管道泄压装置受损、爆管事故等问题,分析了管道水联运过程中不同地形起伏高点和低点带压气团的成因和状态,用实例计算了带压气团对管道水联运的影响及排除方法,从而为长输管道投产前正常的水联运作业提供借鉴。     

Dissipation of Contaminants of Emerging Concern in Biosolids Applied to Nonirrigated Farmland in Eastern Colorado

In 2007, a 1.5‐year field‐scale study was initiated by the U.S. Geological Survey to evaluate the dissipation of contaminants of emerging concern (CECs) following a first agronomic biosolids application to nonirrigated farmland. CECs with the greatest decrease in concentration in the surface biosolids at 180 days post‐application included indole, d‐limonene, p‐cresol, phenol, and skatol. CECs that were present in the largest concentration in 180‐day‐weathered biosolids included stanols, nonylphenols, bisphenol A, bis(2‐ethylhexyl) phthalate, hexahydrohexamethyl cyclopenta‐benzopyran (HHCB), and triclosan. CECs that were detected in pre‐application soil were 3‐beta coprostanol, skatol, acetophenone, beta‐sitosterol, beta‐stigmastanol, cholesterol, indole, p‐cresol, and phenol, most of which are biogenic sterols or fragrances that have natural plant sources in addition to anthropogenic sources, yet their concentrations increased (in some cases, substantially) following biosolids application. Preliminary data indicate the nonylphenols (including NPEO1, NPEO2), OPEO1, benzo[a]pyrene, diethyl phthalate, d‐limonene, HHCB, triclosan, and possibly 3‐beta coprostanol, skatol, beta‐sitosterol, cholesterol, indole, and p‐cresol, migrated downward through the soil by 468 days post‐application, but indicated little uptake by mature wheat plants. This study indicates that some CECs are sufficiently persistent and mobile to be vertically transported into the soil column following biosolids applications to the land surface, even in semiarid regions.     

Large Biases in Regression‐Based Constituent Flux Estimates: Causes and Diagnostic Tools

It has been documented in the literature that, in some cases, widely used regression‐based models can produce severely biased estimates of long‐term mean river fluxes of various constituents. These models, estimated using sample values of concentration, discharge, and date, are used to compute estimated fluxes for a multiyear period at a daily time step. This study compares results of the LOADEST seven‐parameter model, LOADEST five‐parameter model, and the Weighted Regressions on Time, Discharge, and Season (WRTDS) model using subsampling of six very large datasets to better understand this bias problem. This analysis considers sample datasets for dissolved nitrate and total phosphorus. The results show that LOADEST‐7 and LOADEST‐5, although they often produce very nearly unbiased results, can produce highly biased results. This study identifies three conditions that can give rise to these severe biases: (1) lack of fit of the log of concentration vs. log discharge relationship, (2) substantial differences in the shape of this relationship across seasons, and (3) severely heteroscedastic residuals. The WRTDS model is more resistant to the bias problem than the LOADEST models but is not immune to them. Understanding the causes of the bias problem is crucial to selecting an appropriate method for flux computations. Diagnostic tools for identifying the potential for bias problems are introduced, and strategies for resolving bias problems are described.     

一次对流过程对气溶胶清除和再生过程的影响

利用中尺度天气预报和研究模式(WRF)对2007年4月23～24日广东省发生的一次飑线过程进行了模拟,研究了水凝物粒子对气溶胶的清除过程、液滴蒸发引起的气溶胶再生过程以及动力输送作用对气溶胶数浓度的影响.结果表明,降水与近地层气溶胶数浓度的变化呈负相关关系.综合对比各水凝物种类的碰撞清除率和核化清除率,发现云滴核化过程...     

基于四维通量法的佛山臭氧污染输送量化

定量输送过程对大气污染事件的贡献程度一直是目前区域大气污染防控的突出难点和重要需求.对此,基于WRF-Chem模式对佛山典型区域性臭氧(O₃)污染事件开展模拟,应用四维通量法分别量化周边区域对佛山市臭氧及其前体物的输送通量,厘清臭氧直接输送和前体物输送的贡献,发现周边区域对佛山市输送的O₃总通量平均值为120.3 t·h^-1;挥发性有机化合物(VOCs)总通量平均值为30.2 t·h^-1;其对应的臭氧生成潜势(OFP)为114.8 t·h^-1.通过统计各O₃污染事件的输送通量,发现污染期间输入佛山O₃通量最大的城市为广州(贡献率为44%);输入VOCs通量最大的城市为肇庆(贡献率为48%).分析输送VOCs排放导致的O₃生成潜势发现含氧挥发性有机物(OVOCs)对OFP的贡献最大,在“最大输入事件”中占比为47%.甲醛、二甲苯、醛类、丙酮和苯酚类等OVOCs和芳香烃是对OFP贡献前5的物种,贡献量占总OFP的50...     

南京市南部地区O3污染特征、生成敏感性及传输影响分析

作为中国最重要的城市群之一,近年来长江三角洲(YRD)地区大气臭氧(O₃)污染问题突出.于2020年7～9月和2021年4～5月在南京市南部地区溧水站点开展了大气O₃、氮氧化物(NO_x)和挥发性有机物(VOCs)等污染物的在线观测.在此基础上分析了溧水站点O₃的污染特征并与城区站点进行比较,发现溧水站点O₃污染较城区站点更加严重.在观测期间选择了3次典型的O₃污染过程,分别为2020年8月16～27日、 2020年9月3～11日和2021年5月17～25日.利用基于观测的模型(OBM)分析了这3次污染过程的O₃-VOCs-NO_x敏感性.基于OBM所模拟的O₃前体物相对增量反应性(RIR)和NO_x和VOCs削减情景下O₃生成等值线(EKMA曲线)结果显示,3次污染过程中O₃-VOCs-NO_x敏感性分别处于N...     

生物炭与氮肥复施对镉污染水稻土修复效应及机制

重金属镉(Cd)污染已严重影响土壤健康,威胁农产品生产安全利用.因此采用盆栽试验,以Cd污染水稻土为供试土壤,研究生物炭(BC)、不同氮肥施用水平2.6 g·pot^-1(N1)、 3.5 g·pot^-1(N2)、 4.4 g·pot^-1(N3)和生物炭配施氮肥(BCN1、 BCN2、 BCN3)对土壤Cd形态、水稻体内Cd富集和转运以及土壤酶活性的影响,并通过高通量测序分析土壤微生物菌群变化和微生物间复杂的相互作用关系.结果表明,生物炭配施氮肥处理下土壤Cd由活性较高的可交换态向活性低的残渣态转化,可交换态Cd含量较对照降低了6.2%～14.7%,残渣态Cd含量提高了18.6%～26.4%.单一氮肥处理增强了水稻根部Cd的富集能力,提高了22%～33.5%,单一生物炭和配施氮肥处理下水稻根部Cd的富集能力、 Cd从茎叶向稻壳和稻壳向稻米的转运系数均下降.BCN处理总体上促进了土壤酶活性(脲酶、酸性磷酸酶、蔗糖酶和过氧化氢酶),MiSeq测序显示生物炭配施氮肥提高了土壤细菌主要物种相对丰度(如Acidobacteriale...     

基于AIS数据的中国沿海集装箱港口碳排放

为了精确有效地测量船舶在港碳排放,提出了一种基于海量船舶AIS (Automatic identification System)航行轨迹数据的港口碳排放计算框架,并结合上市港务公司经营数据,估算港口碳排放承担能力.以中国11个沿海主要集装箱港口为例,采用2018年全球4280艘集装箱船的AIS轨迹全年数据计算碳排放社会成本.结果显示:中国沿海集装箱港口碳排放量与船舶抵港艘次整体呈正相关,上海港是全球第一大集装箱港口,2018年其港口CO₂排放量最高,为69.3万t;船舶靠港作业时,在泊和锚泊状态CO₂的排放比例较高,占碳排放比例的65.8%;从CO₂排放社会成本来看,上海港域内的船舶碳排放社会成本最高,2018年需要支付2459.6万元,从承担碳排放社会成本的能力来看,连云港压力较大,每亿营业收入需要承担碳排放社会成本24.46万元.     

Cross-boundary transport and source apportionment for PM2.5 in a typical industrial city in the Hebei Province, China: A modeling study

Cross-boundary transport of air pollution is a difficult issue in pollution control for the North China Plain. In this study, an industrial district (Shahe City) with a large glass manufacturing sector was investigated to clarify the relative contribution of fine particulate matter (PM_2.5) to the city's high levels of pollution. The Nest Air Quality Prediction Model System (NAQPMS), paired with Weather Research and Forecasting (WRF), was adopted and applied with a spatial resolution of 5 km. During the study period, the mean mass concentrations of PM_2.5, SO₂, and NO₂ were observed to be 132.0, 76.1, and 55.5 μg/m³, respectively. The model reproduced the variations in pollutant concentrations in Shahe at an acceptable level. The simulation of online source-tagging revealed that pollutants emitted within a 50-km radius of downtown Shahe contributed 63.4% of the city's total PM_2.5 concentration. This contribution increased to 73.9±21.2% when unfavorable meteorological conditions (high relative humidity, weak wind, and low planetary boundary layer height) were present; such conditions are more frequently associated with severe pollution (PM_2.5 ≥ 250 μg/m³). The contribution from Shahe was 52.3±21.6%. The source apportionment results showed that industry (47%), transportation (10%), power (17%), and residential (26%) sectors were the most important sources of PM_2.5 in Shahe. The glass factories (where chimney stack heights were normally < 70 m) in Shahe contributed 32.1% of the total PM_2.5 concentration in Shahe. With an increase in PM_2.5 concentration, the emissions from glass factories accumulated vertically and narrowed horizontally. At times when pollution levels were severe, the horizontally influenced area mainly covered Shahe. Furthermore, sensitivity tests indicated that reducing emissions by 20%, 40%, and 60% could lead to a decrease in the mass concentration of PM_2.5 of of 12.0%, 23.8%, and 35.5%, respectively.     

Source apportionment of polycyclic aromatic hydrocarbons (PAHs) in a sediment core from Lake Dagze Co, Tibetan Plateau, China: Comparison of three receptor models

Receptor models are a useful tool for identifying sources of polycyclic aromatic hydrocarbons (PAHs) in multiple environmental media. In this study, three different receptor models (including the principal component analysis-multiple linear regression (PCA-MLR), positive matrix factorization (PMF), and Unmix models) were used to apportion the sources of 16 priority PAHs in a sediment core of Lake Dagze Co. The ∑PAHs (sum of all 16 measured PAHs) concentrations ranged from 51.89 to 132.82 ng/g with an average of 80.39 ng/g. The ∑PAHs were dominated by 2-3 ring PAHs, accounting for 80.12% on average, thereby indicating that they mainly originated from biomass and coal combustion and/or from long-range atmospheric transportation. The three models produced consistent source apportionment results. The greatest contributor to ∑PAHs was biomass combustion, followed by coal combustion, vehicle emissions, and petrogenic sources. Moreover, the temporal variation of the common sources was well-correlated among models. The multi-method comparison and evaluation results showed that all three models were useful tools for source apportionment of PAHs, with the PMF model providing better results than the PCA-MLR and Unmix models. The temporal trends of factor contributions were verified by PAHs with different ring numbers. Significant correlations were found between the simulated concentrations of each source factor and the PAHs with different ring numbers (P<0.01), except for the petrogenic source identified by the Unmix model (P>0.05). This study can provide useful information for further investigation of source apportionment of PAHs in the sediment cores.