基于静止卫星GOCI传感器的大气污染过程AOD监测

针对北京及周边地区2017年11月2~8日的一次污染过程,利用韩国静止卫星COMs1GOCI数据,对北京地区进行AOD监测.AOD反演采用时间序列迭代算法,根据地表反射率随时间慢变而大气气溶胶随时间快变的理论,采取最小值拟合的方式,获取气溶胶光学厚度数据.反演结果与地基AERONET监测结果具有很好的一致性,两者的相关系数R²大于0.89.AOD监测结果表明,GOCI传感器1次/h的监测频率,可以很好地展现北京地区大气污染过程的开始,发展及消散过程,可以展示出一天之内AOD的变化,为大气污染监测以及气候变化研究提供依据.     

Monitoring Air Quality in Mountains: Designing an Effective Network

A quantitatively robust yet parsimonious air-quality monitoring network in mountainous regions requires special attention to relevant spatial and temporal scales of measurement and inference. The design of monitoring networks should focus on the objectives required by public agencies, namely: 1) determine if some threshold has been exceeded (e.g., for regulatory purposes), and 2) identify spatial patterns and temporal trends (e.g., to protect natural resources). A short-term, multi-scale assessment to quantify spatial variability in air quality is a valuable asset in designing a network, in conjunction with an evaluation of existing data and simulation-model output. A recent assessment in Washington state (USA) quantified spatial variability in tropospheric ozone distribution ranging from a single watershed to the western third of the state. Spatial and temporal coherence in ozone exposure modified by predictable elevational relationships ( 1.3 ppbv ozone per 100 m elevation gain) extends from urban areas to the crest of the Cascade Range. This suggests that a sparse network of permanent analyzers is sufficient at all spatial scales, with the option of periodic intensive measurements to validate network design. It is imperative that agencies cooperate in the design of monitoring networks in mountainous regions to optimize data collection and financial efficiencies.     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.     

日照市大气质量指数分区预报

利用1999年1月至2002年7月日照市环境监测站监测的污染物浓度资料和探空及本站地面气象资料,分析了日照地区可吸入颗粒物(PM_(10))、二氧化硫(SO_2)、氮氧化物(NO_x)浓度变化的时空分布特征及气象要素的变化对污染物浓度时空分布特征的影响。在此基础上,建立了可吸入颗粒物(PM_(10))、二氧化硫(SO_2)、氮氧化物(NO_x)浓度指数预报方程及大气质量指数分区预报流程。并用VB6.0语言编程在微机上建立了简洁流畅、操作简便的大气质量指数分区预报系统。实际预报结果较好。     

湿法吸收测定大气中的五氧化二磷

用２％ＮａＯＨ溶液吸收大气中的Ｐ２Ｏ５，用钼锑抗试剂显色定量。对样品的稳定性、方法的显色条件、精密度、准确度以及检出限进行反复试验，结果令人满意。     

上海市机动车污染实时排放预警系统设计与应用

研究上海市机动车污染的动态排放测算和网格化动态排放清单构建,在实时的交通数据和交通环境监测数据的基础上,结合交通模型、机动车排放清单模型等业务模型和算法,依托大数据存储、可视化和GIS等技术,开发了上海市机动车污染物实时排放预警系统,实现了上海市全市道路的机动车动态排放测算、交通环境政策实施情景模拟和网格化排放清单,更新频率为每30 min一次,包含PM、NOx、CO、SO2、VOCs等污染物和9种车型。系统建成后直接服务于首届中国国际进口博览会,为大气污染排放实时总量跟踪评估、污染源管控措施分析及监测成因分析等提供了有力的实时数据和技术支撑。     

水中氮类污染物的联系与区别

叙述了水体中含氮类污染物的来源，种类，相互转化方式和条件，表征意义等，以便对水中氮类污染物有一个全面，系统的认识。     

阿图什市空气质量变化趋势分析

分析了阿图什市“八五”至“九五”期间空气质量监测数据不同年度、不同季节及不同污染因子的动态变化趋势．结合当地能源结构、气候特征、城市环境综合发展水平，指出影响阿图什市空气质量的主要因素，为防治和减轻阿图什市的空气污染提供了科学依据。     

兰州市功能区大气细菌污染和城市绿地系统杀菌效应分析

在测定兰州市城市不同功能区和不同类型绿地系统空气细菌数量的基础上,分析了各功能区空气细菌污染状况,以及人流量和车流量与空气细菌数量之间的关系;探讨了不同绿地系统的杀菌效应,指出各绿地系统杀菌能力:乔、灌、草>乔、灌>草、灌>草坪。     

Distribution of airborne particles from multi-emission source

The purpose of this work was to study the distribution of airborne particles in the surroundings of an iron and steel factory in southern Finland. Several sources of particulate emissions are lying side by side, causing heavy dust loading to the environment. This complicated multi-pollutant situation was studied mainly by SEM/EDX methodology.Particles accumulated on Scots pine bark were identified andquantitatively measured according to their element content,size and shape. As a result, distribution maps of particulateelements were drawn and the amount of different particle typesalong the study lines was plotted. Particulate emissions fromthe industrial or energy production processes were not themain dust source. Most emissions were produced from theclinker crusher. Numerous stockpiles of the industrial wastesand raw materials also gave rise to particulate emissions as aresult of wind erosion. It was concluded that SEM/EDXmethodology is a useful tool for studying the distribution ofparticulate pollutants.