Analysis of the relationship between ambient levels of O3, NO2 and NO as a function of NOx in the UK

Monitoring data from the UK Automatic Urban and Rural Network are used to investigate the relationships between ambient levels of ozone (O₃), nitric oxide (NO) and nitrogen dioxide (NO₂) as a function of NO_x, for levels ranging from those typical of UK rural sites to those observed at polluted urban kerbside sites. Particular emphasis is placed on establishing how the level of ‘oxidant’, OX (taken to be the sum of O₃ and NO₂) varies with the level of NO_x, and therefore to gain some insight into the atmospheric sources of OX, particularly at polluted urban locations. The analyses indicate that the level of OX at a given location is made up of NO_x-independent and NO_x-dependent contributions. The former is effectively a regional contribution which equates to the regional background O₃ level, whereas the latter is effectively a local contribution which correlates with the level of primary pollution. The local oxidant source has probable contributions from (i) direct NO₂ emissions, (ii) the thermal reaction of NO with O₂ at high NO_x, and (iii) common-source emission of species which promote NO to NO₂ conversion. The final category may include nitrous acid (HONO), which appears to be emitted directly in vehicle exhaust, and is potentially photolysed to generate HO_x radicals on a short timescale throughout the year at southern UK latitudes. The analyses also show that the local oxidant source has significant site-to-site variations, and possible reasons for these variations are discussed. Relationships between OX and NO_x, based on annual mean data, and fitted functions describing the relative contributions to OX made by NO₂ and O₃, are used to define expressions which describe the likely variation of annual mean NO₂ as a function of NO_x at 14 urban and suburban sites, and which can take account of possible changes in the regional background of O₃.     

补播和施肥对高寒退化草地根际细菌群落的影响

对退化草地进行补播和施肥是青藏高原地区常见的生态恢复措施,但其对草地根际细菌群落的影响尚不清楚. 采用高通量测序技术,系统地对采取补播和施肥管理的退化高寒草地根际土壤细菌群落的特征进行研究分析. 结果表明,施肥处理后Shannon指数和Simpson指数显著降低,施肥+补播处理对群落结构影响最明显. 放线菌门（Actinomycetota）、变形菌门（Pseudomonadota）和酸杆菌门（Acidobacteriota）为根际细菌群落的主要优势菌门. 补播、施肥和施肥+补播处理后细菌群落网络复杂程度下降、稳定性增加,且网络以正相关为主. 假单胞菌门（Pseudomonadota）、放线菌门（Actinomycetota）和酸杆菌门（Acidobacteriota）为样地关键物种. Mantel检验结果显示,土壤总氮（TN）、总磷（TP）、速效钾（AK）和硝态氮（NO₃^--N）是驱动细菌群落的主要环境因子. 通过研究高寒退化草地根际土壤细菌群落,可为因地制宜制定高寒退化草地管理措施提供理论依据,并为高寒草地退化的防治提供科学基础.     

基于物联网技术的滑坡监测数据自动采集系统研究∗

针对传统滑坡监测数据采集系统布线困难、自动化程度低、成本高等问题,设计了一种基于物联网技术的滑坡监测数据自动采集系统。该系统以 ZigBee 无线传感器网络、无线通信技术为核心,通过布置在滑坡体上的传感器节点采集数据。利用 ZigBee 协议和互联网将数据实时传输到监控中心,使相关工作人员及时掌握滑坡体信息。 以三峡库区八字门滑坡为例,分析了该滑坡当前监测手段的不足,建立了八字门滑坡的物联网监测系统,分别对系统的硬件组成和软件程序进行设计。针对节点能量供应和数据远程传输问题,提出将太阳能供电系统和北斗短报文模块应用到数据采集系统来弥补野外条件的不足。结合八字门滑坡的地形地质条件,优化了滑坡测点的布置方案,并开发了数据采集界面以实现监测数据的自动采集和直观显示。     

基于双电极传感器的腐蚀电流检测系统抗扰技术

目的 针对金属电化学腐蚀中腐蚀电流检测,设计一种高精度I-V转换的微电流检测电路,突破宽范围微弱电流高精度检测技术。方法 通过分析电路的稳定性,针对100 pA~10 mA的微弱腐蚀电流采集,采用法安级偏置运放实现I-V转换,设计基于ADG708电子开关的8档自动调节电路,并通过24位ADC实现高精度模数转换。针对微弱电流采集中信号易受噪声的影响,设计自适应滤波器用于信号滤波处理。结果 通过MATLAB仿真自适应滤波器的有效性,结果表明,在采集i(k)=2sin(2πk)电流时,误差从0.29 mA(1σ)降低到0.003 9 mA(1σ)。通过电流源校准测试系统精度,在100 pA时误差最大,为4.7%;在10 mA时误差最小,为0.049%。当电流值低于100 nA时,测量误差可以控制在5%以内;100 nA以上时,测量误差可以控制在1%以内。结论 在微弱电流采样中引入自适应滤波器后,系统的采样精度显著提高,突破了宽范围的腐蚀电流采集技术,实现了微弱电流检测。可以将腐蚀电流应用在金属腐蚀速率的评价中。     

提高洪水智能预报中洪峰预报精度方法的研究

针对防洪减灾的实际需要,对如何提高智能网络对洪峰的预报精度问题进行了深入系统的研究,提出了峰值放大修正系数和遗传算法优化网络初始权重相结合的改进算法,历史资料的检验结果表明了这些改进策略的有效性和可靠性.     

智能型地理信息系统（AIGIS）在城市抗震防灾规划中的应用

本文简要介绍地理信息系统GIS的发展概况、主要功能以及在国内外的应用现状.对现有的GIS软件技术加以改进,引进人工神经元网络和模糊综合评判技术,发展了一种智能型的GIS,在我国若干城市的抗震设防区划工作中应用,取得良好的效果。     

融合拓扑信息与气象信息的空气质量预测网络

通过空气质量监测数据对正在形成或即将到来的空气污染进行预测是一项具有重要意义的工作,而空气质量监测站只能检测其周围一定范围内的空气污染情况。为了衡量整个城市的空气污染情况,获取任意时间、任意位置的空气质量信息,结合交叉注意力机制,提出了一种融合拓扑信息与气象信息的空气质量预测网络(CGMIM)。将西安市空气质量监测数据与气象数据转换为图像拼接起来,作为输入信息。在高阶非线性时空动态神经网络(MIM)的基础上引入注意力机制,并增加拓扑图编码器模块,提高模型提取能力以及对空气质量监测数据中的空间特征的利用率。最后,使用时空损失函数替代传统的均方误差损失函数,提高模型对空间关系的关注。结果表明：CGMIM网络模型能够在准确预测的同时,对位置区域合理填充,能够有效提升空气质量监测数据的空间分辨率。     

基于传感器的大气PM2.5高密度网格化监测技术及应用研究

基于光散射原理的PM_2.5传感器在近几年快速发展,中国、美国以及欧盟均发布了关于大气PM_2.5传感器使用的指南。传感器类PM_2.5设备受湿度、温度、颗粒物组成等影响,国内外相关应用重点集中在空气质量监测、预报预测、健康暴露评估及污染源精准识别等领域,中国在基于传感器的PM_2.5高密度网格化监测的设备研发、建设规模及应用等均走在世界前列。研究从PM_2.5传感器设备相关的原理、影响因素、性能指标,以及基于传感器形成的高密度网格化监测体系相关的标准规范、质控校准以及业务应用等方面进行了国内外资料调研,为传感器技术应用于大气精细化环境管理提供支撑。     

Stochastic Transfer Function Model and Neural Networks to Estimate Soil Moisture1

Abstract: A practical methodology is proposed to estimate the three‐dimensional variability of soil moisture based on a stochastic transfer function model, which is an approximation of the Richard’s equation. Satellite, radar and in situ observations are the major sources of information to develop a model that represents the dynamic water content in the soil. The soil‐moisture observations were collected from 17 stations located in Puerto Rico (PR), and a sequential quadratic programming algorithm was used to estimate the parameters of the transfer function (TF) at each station. Soil texture information, terrain elevation, vegetation index, surface temperature, and accumulated rainfall for every grid cell were input into a self‐organized artificial neural network to identify similarities on terrain spatial variability and to determine the TF that best resembles the properties of a particular grid point. Soil moisture observed at 20 cm depth, soil texture, and cumulative rainfall were also used to train a feedforward artificial neural network to estimate soil moisture at 5, 10, 50, and 100 cm depth. A validation procedure was implemented to measure the horizontal and vertical estimation accuracy of soil moisture. Validation results from spatial and temporal variation of volumetric water content (vwc) showed that the proposed algorithm estimated soil moisture with a root mean squared error (RMSE) of 2.31% vwc, and the vertical profile shows a RMSE of 2.50% vwc. The algorithm estimates soil moisture in an hourly basis at 1 km spatial resolution, and up to 1 m depth, and was successfully applied under PR climate conditions.