基于地理国情的城区老工业区搬迁改造监测分析——以大连市瓦房店为例

本文以GF2卫星遥感影像为主要影像数据源,利用2017年城市空间格局变化监测成果、2018年基础性地理国情监测成果及空气污染数据等相关专题资料,对瓦房店老工业区搬迁改造实施前后的地表覆盖变化、用地性质变化、空气污染变化等情况开展监测和综合统计分析,客观评价老工业区搬迁改造实施成效。通过分析发现,瓦房店老工业区非城市建设用地逐步变更为居住用地,新的城市绿化景观正在形成,空气质量有变好的趋势,老工业区搬迁改造试点成效显著。     

基于无人机的PM_(2.5)监测技术研究

作为当前我国迫切需要解决的区域性环境问题,灰霾污染引起社会各界广泛关注。而雾霾形成的原因与机理,以及其水平扩散和垂直输送的规律则是解决灰霾问题过程中最为基础和重要的一环。但传统的大气污染监测手段缺乏空气污染垂直分布特征的观测,地面观测数据难以满足PM_(2.5)等大气污染的预报预警工作的需要和区域污染防控对策制定和污染减排工作的要求。本文通过无人机搭载微型空气质量检测器,实时检测不同时间、不同地点、不同高度的大气污染的连续数据,从而有效补充了固定监测站的数据缺失;进而研究大气污染物PM_(2.5)的垂直分布、垂直扩散等特征,并分析不同天气条件下PM_(2.5)的分布特征和扩散机制,从而为建立模型,环境评估和预测提供技术支撑。     

无人机三维空气质量监测研究

针对现有空气监测缺乏污染垂直分布特征的观测及数据采集覆盖范围有限的问题,该文提出了用无人驾驶飞机搭载微型空气质量检测器监测大气污染的方法,首先分析了传统地面监测站的缺点,并阐述了监测设备的构成;然后从无人驾驶飞机大气污染空间采样方案、污染物数据的准确性及可靠性校正、大气污染时空规律几个方面做了研究;最后选择浙江省临安市青山湖街道、上海奉贤区等地区进行了试验。研究结果能有效补充地面监测站的数据缺失,揭示PM2.5、O3等大气污染物的垂直分布、垂直扩散及区域性输送特征,为空气污染预警和防控对策机制的制定提供重要技术依据。     

基于GIS的城市空气污染综合分析

在城市空气污染的模型研究中,GIS可以作为复杂环境模型的输入数据处理器,而且GIS有一个重要的功能模块——统计分析模块,GIS能够对模型的运算输出数据进行综合处理,并对结果进一步分析,进行有关的查询以及对研究成果的可视化输出。在基于GIS的城市空气污染的综合分析中,我们探究了城市土地利用现状对空气质量的影响,本文采用模糊综合分析法探索城市三大污染源在空气污染中所占的比重,制作城市空气污染等级分布图。GIS在大气污染研究领域可以充分发挥其空间分析和可视化优势,为治理工作提供技术路线。     

兰州北山区削山造地扬尘浓度预测方法及应用

土地资源稀缺已经成为一些丘陵山区城市发展的重要制约因素,削山造地也成为这些城市获取土地资源最有效的途径。针对兰州市2013—2017年土地资源扩张现状,利用遥感影像数据、地面高程数据、气象观测数据和空气质量监测等数据,提出了一种将遥感变化监测技术、风蚀模型和Aermod模型三者结合的兰州北山区削山造地扬尘浓度预测方法。试验结果表明,兰州北山区削山造地区域在风蚀作用的影响下产生的扬尘污染会对兰州市区空气质量产生影响,且影响程度主要由削山造地开挖面积、风向风速及削山造地区域与城区距离3个因素决定。当削山造地规模下降、开挖区域重点向北转移后,兰州市区内空气质量所受影响呈下降趋势,但兰州新区附近区域的空气质量所受影响依旧严重。     

基于星间链路的完好性参数SISMA计算方法

地基完好性监测是利用四个或四个以上的精确坐标的监测站对卫星进行监测,计算其星历及星钟误差。该方法需满足四重以上监测站覆盖条件,对于自主导航和区域布站的导航系统不能实现导航卫星的全弧段完好性监测。对于不满足上述覆盖条件的卫星,采用基于星间链路的监测方法,利用四个或四个以上的具有一定坐标误差的卫星对该卫星进行监测。通过星历数据和星间测距数据计算SISMA,从而进行导航卫星全弧段的完好性监测。     

一种网络城市空气质量地理信息服务系统

针对传统地理信息服务系统缺乏准确揭示城市空气污染物时空分布特征能力,无法满足公众及时了解污染信息、协助环境部门制定防控措施的不足,该文设计并初步开发了一种综合GIS技术、Web技术与相关环境、健康风险评估模型的城市空气质量地理信息服务系统。该系统不仅可实现常规空气污染监测数据、人口数据、道路数据、土地利用数据等的可视化、统计分析与制图,也可用于基于上述数据的区域空气污染高空间分辨率模拟制图、人口暴露风险评估和公众出行路线选择。     

京津冀地区污染过程的气溶胶遥感反演

针对地面监测站覆盖范围有限且成本高的不足,该文采用遥感卫星与地面站点数据相结合的方式,利用简化的气溶胶反演算法反演京津冀地区2013年秋、冬季节两次污染过程的气溶胶光学厚度,分辨率为500m。以气溶胶自动观测网在北京站点的监测数据作为简化的气溶胶反演算法的输入参数,分析气溶胶光学厚度与气溶胶自动观测网对应地面站点的相关性;将所反演气溶胶光学厚度与京津冀地区空气质量监测站点的细颗粒物浓度24h均值进行相关性分析,发现除近海城市外,相关性均较高。结果表明,简化的气溶胶反演算法适用于区域污染过程中的气溶胶光学厚度反演,反演精度高,对空气质量具有较好的监测能力。     

武汉后湖地区地面沉降监测系统实现

基于北斗高精度定位和GIS集成技术,研发了一套北斗地面沉降监测系统。以武汉后湖地区为应用示范,通过在该区域内合理布设北斗参考站网和监测站网,建设北斗数据处理中心,利用接收到的北斗观测数据进行处理和解算,对该区域的地面沉降进行监测。定位精度实时可达cm级,事后可达mm级。该系统可以针对城市地面沉降进行多级预警和趋势分析,为城市建设管理提供决策依据。     

运用贝叶斯方法的PM2.5浓度时空建模与预测

针对当前我国大部分地区正面临严重的空气污染问题,对重污染区域进行时空建模具有重要的意义。该文基于贝叶斯时空模型建立了京津冀区域的PM2.5浓度时空预测模型,该模型充分考虑了PM2.5浓度的时间变异特性与空间分布特性,并引入了气象数据作为协变量对没有监测站的位置进行预测。实验结果表明,该方法具有很好的预测精度,其在测试站点上的拟合优度达到了0.9以上,能够应用于区域级PM2.5浓度的时空分布建模与预测。     

加色法同位规则挖掘支持下的兰州市居民区生活便利度分析

居民区生活便利度分析是城市研究的热点问题之一，对提升城市居民生活质量具有重要意义。城市空间中的居民活动主要发生在道路网络空间，加色法同位规则是一种网络空间同位模式可视化挖掘方法，避免了复杂的数学公式、算法及相关参数，具有形象直观的特点。鉴于此，本文利用加色法同位规则这一可视化数据挖掘手段，在表达空间交互的同时将数据挖掘的结果直接用于居民区生活便利度分析。以兰州市为例，进行居民区与其他14类城市基础设施之间位置关系的挖掘试验，全局分析与局部分析相结合，对不同区域的居民区生活便利度进行评价，以期为兰州市城市建设提供参考。结果表明：在兰州市，居民区与药店、超市、牛肉面馆等地理要素存在最强同位规则，与医院、火锅店存在较强同位规则，与商场、派出所、消防站无同位规则；居民区在整体上呈现多个宜居中心的分布特点，在区位上呈现便利度东高西低的分布特征。     

Air pollution hazard assessment using decision tree algorithms and bivariate probability cluster polar function: evaluating inter-correlation clusters of PM10 and other air pollutants

ABSTRACT

The automated classification of ambient air pollutants is an important task in air pollution hazard assessment and life quality research. In the current study, machine learning (ML) algorithms are used to identify the inter-correlation between dominant air pollution index (API) for PM10 percentile values and other major air pollutants in order to detect the vital pollutants’ clusters in ambient monitoring data around the study area. Two air quality stations, CA0016 and CA0054, were selected for this research due to their strategic locations. Non-linear RPart and Tree model of Decision Tree (DT) algorithm within the R programming environment were adopted for classification analysis. The pollutants’ respective significance to PM10 occurrence was evaluated using Random forest (RF) of DT algorithms and K means polar cluster function identified and grouped similar features, and also detected vital clusters in ambient monitoring data around the industrial areas. Results show increase in the number of clusters did not significantly alter results. PM10 generally shows a reduction in trend, especially in SW direction and an overall minimal reduction in the pollutants’ concentration in all directions is observed (less than 1). Fluctuations were observed in the behaviors of CO and NO_x during the day while NOx displayed relative stability. Results also show that a direct and positive linear relationship exists between the PM10 (target pollutant) and CO, SO₂, which suggests that these pollutants originate from the same sources. A semi-linear relationship is observed between the PM10 and others (O₃ and NO_x) while humidity shows a negative linearity with PM10. We conclude that most of the major pollutants show a positive trend toward the industrial areas in both stations while tra?c emissions dominate this site (CA0016) for CO and NOx. Potential applications of nuggets of information derived from these results in reducing air pollution and ensuring sustainability within the city are also discussed. Results from this study are expected to provide valuable information to decision makers to implement viable strategies capable of mitigating air pollution effects.     

顾及手机基站分布的核密度估计城市人群时空停留分布

为了减小人群在连续空间上停留分布的估计误差,结合手机基站的空间的分布特点,根据基站间的邻近性来计算带宽控制参数,使搜索带宽随着基站的分布而变化;利用最小二乘交叉验证和对数概率两种方法来评价其估计效果,结果表明变化带宽比固定带宽的核密度估计效果更优。以深圳市手机位置数据为例,利用改进方法估计了几个典型时段城市人群停留的时空分布差异,反映了城市人群对城市不同区域的使用情况及其随时间变化情况。     

环境空气质量系列专题地图制图方法研究

环境与发展是当今世界共同关注的重大问题。目前我国经济正处于高速发展时期,随着经济社会的快速发展和城镇一体化进程的加快,空气污染问题日趋严重,对人体健康造成很大伤害,因此环境保护的重要性日益突出。本文通过建立环境质量空间数据库,利用地理信息软件ArcGIS为基础平台,将环境空气质量监测数据与地理空间位置进行地理匹配,利用地理信息可视化技术,实现环境空气质量监测数据中各主要空气污染物的浓度时间分布的可视化表达,制作环境空气质量专题地图,有助于有效地从海量监测数据中发现有价值的信息,为环境保护部门提供决策参考依据。     

Building an environmental quality index for a big city: a spatial interpolation approach combined with a distance indicator

One of the main tasks of regional and environmental economics is to construct Environmental Quality Indexes for big cities. A standard method is to generate a single measure as a linear combination of several contaminants by applying Principal Component Analysis. Spatial interpolation is then carried out to determine pollution levels across the city. We innovate on this method and propose an alternative approach. First, we combine a set of noise and air pollutants measured at a number of monitoring stations with data available for each census tract. This yields a mixed environmental index that is socioeconomically more complete. We then apply kriging to match the monitoring station records to the census data. Finally, we construct a composite pollution index using the Pena Distance method (DP2), which proves more robust than traditional approaches.     

Design of an Integrated System for Modeling of Functional Air Quality Index Integrated with Health-GIS Using Bayesian Neural Network

Air pollution is a major problem, conscious both for health and surroundings. This is a novel approach for the design & development of a system for the monitoring of different air pollutants especially at remote places where it is difficult to install any conventional air quality monitoring stations as well as for the cities. In this research work, a framework of Functional air quality index which is an indicator of susceptibility to respiratory illness has been built using the Bayesian neural network to provide the random real-time data about a location through wireless communication. The monitoring system is integrated with different types of sensors to measure the level of different air pollutants or air quality parameters such as Suspended particulate matters, (PM_2.5), Nitrogen dioxide, Sulphur dioxide, Ozone which are directly associated with airways inflammatory diseases such as Asthma, Bronchitis, COPD. Each location in Map (GPS) can be updated automatically with fAQI to the user through mobile computing and satellite commutation. The user gets information about the neighborhood location with health-related information such as- whether a particular location is sensitive to respiratory diseases such as Bronchitis, asthma, COPD etc. due to suspended allergen/pollutants in the ambient air. This novel approach is designed with its’ own prototype and an application of Inter of Things in Health GIS for the benefit of humanity.     

基于三维大气探测激光雷达的大气颗粒物污染探测

激光雷达是一种监测大气颗粒物分布和传输的有效遥感手段，能够克服常规地面监测站零星分布、无法实现区域覆盖监测的问题。通过布设一台垂直探测激光雷达和一台水平扫描激光雷达，实现大气颗粒物的垂直结构探测以及半径6 km内水平分布情况监测，并结合斜率法和Fernald算法实现更加精确的水平方向消光系数反演，进而实现以三维数据分析颗粒物的传输、分布和浓度变化情况；并利用地面国控站点数据同水平消光系数进行相关性对比。结果表明，利用三维大气探测激光雷达能够有效地揭示城市地区较大区域内的颗粒物分布和传输情况，具有覆盖范围大、探测效率高的优点。     

The grey-green divide: multi-temporal analysis of greenness across 10,000 urban centres derived from the Global Human Settlement Layer (GHSL)

ABSTRACT

The presence of green spaces within city centres has been recognized as a valuable component of the city landscape. Vegetation provides a variety of benefits including energy saving, improved air quality, reduced noise pollution, decreased ambient temperature and psychological restoration. Evidence also shows that the amount of vegetation, known as ‘greenness’, in densely populated areas, can also be an indicator of the relative wealth of a neighbourhood. The ‘grey-green divide’, the contrast between built-up areas with a dominant grey colour and green spaces, is taken as a proxy indicator of sustainable management of cities and planning of urban growth. Consistent and continuous assessment of greenness in cities is therefore essential for monitoring progress towards the United Nations Sustainable Development Goal 11. The availability of multi-temporal greenness information from Landsat data archives together with data derived from the city centres database of the Global Human Settlement Layer (GHSL) initiative, offers a unique perspective to quantify and analyse changes in greenness across 10,323 urban centres all around the globe. In this research, we assess differences between greenness within and outside the built-up area for all the urban centres described by the city centres database of the GHSL. We also analyse changes in the amount of green space over time considering changes in the built-up areas in the periods 1990, 2000 and 2014. The results show an overall trend of increased greenness between 1990 and 2014 in most cities. The effect of greening is observed also for most of the 32 world megacities. We conclude that using simple yet effective approaches exploiting open and free global data it is possible to provide quantitative information on the greenness of cities and its changes over time. This information is of direct interest for urban planners and decision-makers to mitigate urban related environmental and social impacts.