一种基于地形因素的空气质量空间估计方法

空气质量监测对于污染评估、危害降低和环保治理等具有重要的指导意义。然而,由于空气质量监测站点的数量十分有限,且空气质量随位置的变化是非线性的,因此空气质量空间估计(即估计任意无空气质量监测站点位置的空气质量)是一项具有挑战性的工作。目前最先进的空气质量空间估计方法考虑了交通、人流、POI等因素,并基于机器学习技术建立估计模型。然而,这些方法仍存在如下不足:1)由于考虑的因素主要反映城区的特性,因此只能局限在城区范围内使用;2)直接使用从各类因素中提取的特征建立模型,没有对特征进行更深层次的提炼。针对上述问题,提出了一种基于地形因素的空气质量空间估计方法。在该方法中,首先建立地形数据库并提取地形特征,然后基于集成决策树模型对地形特征进行深层转换,最后基于因子分解机建立回归模型。基于真实数据的实验表明,该方法对估计自然地形(如高原、森林、水域等)区域中的空气质量有明显的优势。     

一种大数据环境下的在线社交媒体位置推断方法

随着在线社交媒体的快速发展和可定位设备的大量普及,地理位置作为社交媒体大数据中一种质量极高的信息资源,开始在疾病控制、人口流动性分析和广告精准投放等方面得到广泛应用.但是,由于大量用户没有指定或者不能准确指定位置,社交媒体上的地理位置数据十分稀疏.针对此数据稀疏性问题,提出一种基于用户生成内容的位置推断方法UGC-LI(user generate content driven location inference method),实现对社交媒体用户和生成文本位置的推断,为基于位置的个性化信息服务提供数据支撑.通过抽取用户生成文本中的本地词语,构建一个基于词汇地理分布差异和用户社交图谱的概率模型,在多层次的地理范围内推断用户位置.同时,提出一个基于位置的参数化语言模型,计算用户生成文本发出的城市.在真实数据集上进行的评估实验表明:UGC-LI方法能够在15km偏移距离准确定位64.2%的用户,对用户所在城市的推断准确率达到81.3%;同时,可正确定位32.7%的用户生成文本发出的城市,与现有方法相比有明显的提高.     

An assessment of air-quality monitoring station locations based on satellite observations

ABSTRACT

Optimization of the locations of air quality monitoring stations has great importance in providing high-quality data for regional air pollution monitoring. To assess the representativeness of the locations of the current air quality monitoring stations, we propose a new method based on satellite observations by applying the stratified sampling approach. Unlike the traditional method, which relies on the simulated spatial distribution of air pollutants from dispersion models, we obtained the sampling population through observations from remote sensing. As a first step, the spatial distribution of aggregated air quality was obtained based on ground concentrations of particulate matter (aerodynamic diameters of less than 10 μm, PM₁₀), fine particulate matter (aerodynamic diameters of less than 2.5 μm, PM_2.5), nitrogen dioxide (NO₂), and sulphur dioxide (SO₂) derived from satellite observations. Second, the representativeness of locations of air quality monitoring stations was assessed using the stratified sampling method. The results demonstrated that air quality monitoring stations in Beijing-Tianjin-Hebei were clustered in areas with heavily polluted air, whereas the number of air quality monitoring stations was insufficient in areas with higher air quality. After optimization, the minimum relative error was only 6.77%. It is indicated that combing remote-sensing data with the stratified sampling approach has great potential in assessing the spatial representativeness of air quality monitoring stations.     

省级气象资料传输监控平台的设计及实现

气象资料是开展气象预报、气象服务的根基。针对省级气象资料传输切换到CIMISS（China Integrated Meteorological Information Service System）系统后市县级缺乏有效资料监控手段的问题，在分析气象资料传输流程的基础上，本文给出气象资料传输监控平台的总体设计方案，提出利用时间单元偏移的方法来进行监控日志采集，完成数据库设计方案以及平台展示方案设计。监控平台采用MVC框架以及HTML、AJAX等技术实现气象资料的传输监控和质量统计等功能。平台面向省市县三级用户，为改进省级气象资料的传输质量提供可靠的技术保障。     

Meteorological effects on sulphur dioxide concentrations on the area of Turbigo

This paper presents the relationship between meteorological parameters and sulphur dioxide concentrations in the area of Turbigo. A large number of sulphur dioxide sources are located in this area: they are both urban and industrial emissions. Mean half hourly SO₂ measurements were recorded over a three year period at five monitoring stations. Meteorological data were taken by a meteorological station in this network and included mean halfhourly measurements of wind direction and speed, air temperature, atmospheric pressure, relative humidity and rainfall. The SO₂ data were classified by the meteorological parameters, singly and in combination. From the analysis of the effects of various meteorological parameters, wind direction was found to be the parameter best correlated with pollution concentration. Additional results regarding the seasonal cycles of pollution levels are also presented. The work is also based on a set of statistical and graphic techniques.     

Estimation of urban air pollution

The problem of real-time estimation of air pollutant concentrations in an urban atmosphere based on concentration measurements made intermittently at a set of monitoring stations is considered. A square-root distributed parameter filter for a general class of dynamic urban air pollution models is developed. The filter is tested by application to a hypothetical urban area, and the effect of the number of monitoring stations on the estimated concentrations is studied.     

基于GCN-LSTM的空气质量预测

随着我国环境监测技术的不断发展,环境空气质量的网格化监测体系越来越受到相关工作人员的青睐,为应对空气污染的网格化监测体系中的小型、微型监测站的空气质量预测问题,本文提出了一种基于GCN和LSTM的空气质量预测模型.首先利用GCN网络提取网格化监测体系中的小微型监测站之间的空间特征,然后再使用LSTM提取时间特征,最后使用线性回归层来综合时空特征并产生空气质量的预测结果.为了验证本文提出的预测模型的性能,我们使用了沈阳市浑南区的14个小微型监测站的空气质量监测数据进行实验.实验结果显示,基于GCN-LSTM的空气质量预测模型在空间关联较强的网格化监测中的小微型监测站上的预测结果的精度要优于单一的LSTM预测模型.     

Investigating the sensitivity of health benefits to focussed PM2.5 emission abatement strategies

Modelling, pollution monitoring and epidemiological studies all have a role to play in developing effective policies to improve air quality and human health. Epidemiological studies have shown that of particular importance are the effects of fine particulate matter, PM₁₀ and PM_2.5 which can penetrate into human lungs. At present it is not clear which components of PM are responsible for health effects although toxicological studies have identified several potential factors. Hence, based on WHO guidance, current legislation has focused on the total mass, with the EC setting limit values on total PM₁₀, followed by target reductions for population exposure to PM_2.5 in urban agglomerations. Trends in measured concentrations at selected urban monitoring stations are required as evidence for achievement of these reductions. This paper addresses these issues at the borough level in London using the integrated assessment model UKIAM, developed originally for application at the national scale, with illustrations comparing abatement of two contrasting sources – domestic combustion and road transport. The former, dominated by natural gas generating NO_X emissions, contributes to longer range secondary PM formation extending beyond the city. The latter is an important source of black carbon as a primary pollutant causing local exposure, as well as NO_X. WHO data is used in relation to impacts of particle concentrations by mass, and response functions for black carbon are taken from the literature. The results show that from a city perspective there are enhanced benefits from reducing the road transport emissions, especially with regard to potential toxicity of black carbon. The scenarios modelled also highlight the spatial variations of benefits across London, and illustrate deviations from trends as represented by limited monitoring data from the different boroughs, together with the influence upon exposure of mobile population within the city.     

An evolutionary system for ozone concentration forecasting

Nowadays, with more than 50 % of the world’s population living in urban areas, cities are facing important environmental challenges. Among them, air pollution has emerged as one of the most important concerns, taking into account the social costs related to the effect of polluted air. According to a report of the World Health Organization, approximately seven million people die each year from the effects of air pollution. Despite this fact, the same report suggests that cities could greatly improve their air quality through local measures by exploiting modern and efficient solutions for smart infrastructures. Ideally, this approach requires insights of how pollutant levels change over time in specific locations. To tackle this problem, we present an evolutionary system for the prediction of pollutants levels based on a recently proposed variant of genetic programming. This system is designed to predict the amount of ozone level, based on the concentration of other pollutants collected by sensors disposed in critical areas of a city. An analysis of data related to the region of Yuen Long (one of the most polluted areas of China), shows the suitability of the proposed system for addressing the problem at hand. In particular, the system is able to predict the ozone level with greater accuracy with respect to other techniques that are commonly used to tackle similar forecasting problems.     

基于社区网络的大气污染源定位算法

污染源定位是大气污染治理与预防中的重要环节.为了避免地表状况、温度和风向等环境条件对污染源定位的影响,提出一种基于社区网络分析的污染源定位算法.通过Granger因果检验方法分析各监测点的空气质量指数AQI的时间序列,得出任意两个监测点的AQI值之间的影响关系.以监测点作为节点,以影响关系作为监测点间的关联关系,构建污...     

Quantify city-level dynamic functions across China using social media and POIs data

Location-aware big data from social media have been widely used to study functions of different zones in a city but not across a city as a whole. In this study, a novel framework is proposed to quantify city-level dynamic functions of 200 cities in China from a perspective of collective human activities. The random forest model was used to determine the temporal variations in the proportions of different urban functions by examining the relationship between Points-of-Interest (POIs) and Tencent Location Request (TLR) data. We then hierarchically clustered and analyzed the structures and distribution patterns of the dynamic urban functions of 200 Chinese cities at different temporal scales. In the end, we calculated an urban functional equilibrium index based on the urban functional proportion and then mapped spatial distribution patterns of the indexes across mainland China. Results show that on a daily scale when the cities were grouped into two clusters, they are either dominated by the work/education and commerce or residence functions. The cities in the former cluster are mainly the provincial capitals and located within major urban agglomerations. When the cities were grouped into four clusters, the clusters are dominated their commerce, work, residence, and balanced multiple functions, respectively. For each of the 200 cities, its urban functions change dynamically from the daybreak to the evening in terms of human activities. Besides, the equilibrium indexes show a power-law relationship with their rankings. Our research shows that city-level dynamic function can be quantified from the perspective of variations in human activities by using social media big data that otherwise could not be achieved in the conventional urban functions’ studies.     

基于随机森林和气象参数的PM2.5浓度等级预测

空气污染不仅危害人类的身心健康,而且还会制约城市的经济发展,其中PM2.5带来的影响尤为突出。为了方便准确地预测出空气中的PM2.5浓度等级,提出了一种基于随机森林的PM2.5浓度等级预测方法,特征因子采用太原市2013年-2017年的气象数据、预测站点的PM2.5浓度变化的时间规律以及与周围站点的时空关联性。该方法首先利用K-Means算法对原始气象数据聚类,降低不同分类器之间的相关性,然后利用欠采样方法对数据进行平衡采样,减少类不平衡对分类器性能的影响,最后利用泛化能力好的随机森林构建预测模型。经过真实数据验证,该方法对PM2.5浓度等级预测具有较好的精确度、召回率与[F]值。     

Analysis of spatial and seasonal distributions of air pollutants by incorporating urban morphological characteristics

Due to the worldwide trend of industrialization and urbanization, air pollutants were emitted heavily on a global scale particularly in developing countries, which produces adverse effects on human health by causing health problems such as respiratory and lung diseases. Many regression models based on land use types and urban fabrics have been built to analyze the spatiotemporal distribution of air pollutants, however, few of them examined the relationship between urban morphological characteristics and the distribution of air pollutants in a megacity. This study investigates such relationships for six types of air pollutants (PM2.5, PM10, SO₂, NO₂, O₃, and CO) and a composite AQI (Air Quality Index) based on hourly data at 35 monitoring stations in Beijing in 2016, with morphological characteristics (Morphological building index), meteorological factors (Land Surface Temperature, LST), land use (vegetation, road length, gas station and industry point data), and population distribution data. We also analyzed the results with spatiotemporal regression and SSH (Spatial Stratified Heterogeneity) models respectively. According to the spatiotemporal regression model, the morphological building index (MBI) shows a strong correlation with the dispersion of PM2.5 (R² = 0.81) and AQI (R² = 0.80) in the warm season and this finding was reinforced through the Leave-one-out-cross-validation (LOOCV) analysis. From the SSH analysis, the road length in a large proximal region impacts air pollutants the most, especially for O₃; and population density significantly affects PM 2.5, AQI, SO₂, and NO₂ in the cold season. From an integrated interpretation, distance to nearest industry impacts the spatial distribution of NO₂ in cold season, while it impacts that of PM2.5 and AQI in both warm and cold seasons. The research finds that these two models supplement each other well and together help to give us a better understanding of how air quality is affected in the urban landscape.     

Real-time urban power dispatch with ambient air quality constraints

A power dispatch methodology for urban utility company facing ambient air quality constraints is developed. This approach consists of (1) a real-time air dispersion model for the urban area and its surrounding region, (2) a fuel minimization procedure which will generate a schedule to satisfy the demand and the constraints on pollution concentration. Forecast meteorological data and ambient air quality information are assumed available for the procedure.     

基于时空优化深度神经网络的AQI等级预测

针对现有空气质量预测方法精度偏低、对噪声敏感等问题,提出一种基于堆栈降噪自编码（Stacked Denoising Auto-Encoders,SDAE）模型的空气质量等级预测方法。首先以武汉市历史空气质量和气象监测数据为研究对象,建立SDAE模型逐层学习原始数据的特征表达,并将最后一层特征与分类器连接完成预测模型的调优。同时改进多参数网格搜索法,选取了最优的超参数组合。然后在测试集上进行预测,并用预测值与实际值之间的平均绝对误差和均方误差等指标作为预测性能评价标准。通过与其他网络模型的实验对比,证明了SDAE模型对于空气质量等级具有较优的预测性能。最后从时间、空间、时空三个角度对该模型输入进行优化,实验结果表明基于空间优化的SDAE模型预测性能提升最为明显,能够得到比传统方法更加精确的预测结果。     

Optimization model for urban air quality policy design: A case study in Latin America

Air pollution is a major problem caused by urban growth and poses a high risk to human health. To mitigate the negative impact of pollution, some cities have implemented air quality strategies that regulate emissions from multiple sources. However, when such strategies are applied in isolation they may fail to comply with stringent environmental goals. To support the development of comprehensive urban air quality policy, we propose an optimization model that suggests a portfolio of mitigation actions that captures realistic aspects of air quality policy-making such as implementation cost, pollution goals, interdependencies between alternatives, and managerial and regulatory constraints. We illustrate the advantages of our model with a real case study in Bogotá, Colombia's capital and one of the largest cities of Latin America, where new legislation established a PM₁₀ air quality standard. To achieve the environmental goal, our model suggests an optimal portfolio consisting of six mitigation actions targeting mobile and stationary sources. Interventions in the transport sector alone generate 70% of the emissions reduction. The PM₁₀ goal is achieved with a net incremental cost of US$1 billion with respect to the no-intervention scenario. The optimization model and results presented in this article were formally adopted as the official air quality policy in the city.     

基于社交网络群智感知信息的非常规突发事件描述方法研究

群智感知是以近年来兴起的物联网、大数据、云计算等多种技术为基础发展起来的。群智感知就是用城市环境中感知的大数据来解决城市本身所面临的挑战,通过对多源异构数据的整合、分析和挖掘来提取知识和价值,从而提高城市服务的现代化水平。非常规突发事件是一类前兆特征不充分、具有明显的复杂性和潜在次生衍生危害、破坏性严重、采用传统管理方式难以应对处置的罕见重大突发事件。由于构成群智感知网络的基本单元是无所不在的移动智能终端用户,这种新型的体系架构无论是在感知及认知的广度、深度还是在构建的成本与速度上,都是传统技术手段所无法比拟的。对社交网络信息进行语义感知、时空关联等以对非常规突发事件进行描述,并通过实例对该方法进行验证。非常规突发事件的语义信息包括关键词、模式、语句等。时空信息用来描述非常规突发事件的客观时间属性与空间属性。时间属性包括非常规突发事件的发生时间、结束时间,以及重要时间点。空间属性包括非常规突发事件的发生位置、影响位置,位置信息可以是路名建筑物名称,也可以具体到GIS信息。多媒体信息包括图像、视频、音频等。     

基于图像的空气质量等级检测

目前国内主要依靠各种精密仪器检测空气中的污染物浓度.由于仪器的成本较高, 国家通过在每个城市设立监测站来检测空气质量, 这种空气质量检测方法是粗粒度的, 不能覆盖城市的每个角落.本文提出了一种基于图像的空气质量等级检测方法, 旨在通过移动设备采集的图像检测空气质量等级, 移动设备的普及使得通过图像细粒度检测空气质量成为可能, 该方法利用空气污染对图像颜色通道和灰度通道局部信息熵的影响构建空气质量等级检测模型.在本文构建的空气质量图像库进行了模型测试和比较分析, 实验结果表明:本文方法能够准确地评估空气质量等级, 比其他已有相关方法更适用于空气质量等级检测.     

基于GIS的城市烟气在线监控系统的设计与实现

文章介绍了一个基于GIS的城市燃煤烟气在线监控系统的设计与实现。该系统融合了INTERNET、数据库和GIS等技术,实现了在环境监控中心对分散在城市不同位置的重点污染源烟气排放的远程监测、对远端监测仪器的状态监控、对监测业务数据的常规管理和对监测数据的分析可视化处理等功能。利用该系统,将为环境监测与管理部门及时把握城市重点污染源排放状况及监测仪器运行状况并采取必要的控制措施提供支持。