大气污染暴露风险防控可视化决策支持平台

针对当前我国大气污染防治正逐步由污染治理转向风险防控,而现有空气质量监测设备和平台服务仅限于环境监测而非暴露监测的问题,设计研发了一套基于B/S架构的可视化综合分析与决策支持平台——大气污染暴露风险测量系统（APERMS）。首先,基于大气污染浓度监测数据和暴露时空行为活动模式,耦合集成污染浓度制图、个体暴露测量、人群暴露测量、暴露风险评价这一完整的大气污染暴露风险测量技术路线;其次,基于高可用和可靠原则,进行系统的总体架构设计、数据库设计和功能模块设计;最终,采用GIS与J2EE Web等技术,完成APERMS开发,实现了大气污染浓度分布高时空分辨率模拟、个体和人群大气污染暴露状况精准评估、大气污染暴露风险水平全方位评价等功能。APERMS主要应用于大气污染监控和环境健康管理行业,为风险规避和污染防控提供有效的技术支持。     

Healthier routes planning: A new method and online implementation for minimizing air pollution exposure risk

Air pollution exposure during daily traveling is growing as an increasingly serious factor affecting public health with rapid incensement of travel distance in urban sprawl. Finding a healthier route with least exposure risk might be an alternative way to mitigate adverse health outcomes under the truth that worldwide air pollution in urban area cannot be eliminated within a short period of time. Integrating techniques of fine scale mapping of air pollutant concentration, risk weight estimation of road segment exposure to air pollutants, and dynamic Dijkstra algorithm capable of updating route, this study for the first time proposes a healthier route planning (HRP) method to minimize personal travel exposure risk to air pollution. Effectiveness of HRP in mitigating exposure risk was systematically tested based on hundred pairs of origins and destinations located in Beijing-Tianjin-Hebei (BTH) of China with necessarily dense air quality observations. Results show that the spatiotemporal variations of air pollutant concentrations were significant and these differences indeed occurred with time at hourly scale. Meanwhile, the grid-based estimation of exposure risk is time dependent with risk ranging from 5 to 109, which echoes the necessity of healthier route planning. Compared to routes with the shortest distance and least travel time, healthier route has the least exposure risk. And this risk mitigation effect is more significant in areas with wide exposure risk variations than those in areas without obvious risk difference over space (e.g., 21.38% vs. 0.86%). Results suggest that HRP method is promising to minimize personal exposure risk during daily travel based on the accurate exposure risk estimation of road segment at high spatiotemporal resolution. This role could be more important in areas with longer travel distance and greater heterogeneous distribution of air pollution in great metropolis.     

室外空气污染暴露规避健康路径规划服务

日常出行吸入空气污染物是公众空气污染暴露风险发生的主要途径之一。在我国当前仍有75.1%的城市环境空气质量超标背景下,如何有效降低室外日常出行空气污染暴露强度成为了公众防控大气污染健康损害的一种新需求。集成普通克里格空间插值方法、暴露剂量评估模型、Dijkstra路径搜索算法,设计与开发了面向Android智能手机终端的公众健康路径规划应用程序（APP）,实现了空气污染浓度动态变化情景下以暴露剂量为指标的健康路径出行规划功能。以室外PM2.5暴露为例的测试结果表明,APP服务规划下的健康路径相比最短路径和最快路径可分别降低出行个体5.0%和7.3%的暴露剂量,是一种公众规避空气污染暴露风险的有效路径规划服务。     

An optimization platform for Poland's power sector considering air pollution and health effects

The article demonstrates the features and applicability of the πESA platform designed for optimization of the Poland's power sector considering air pollution and health effects. πESA is comprised of: a bottom-up energy-economic model TIMES-PL, an air quality modelling system Polyphemus and a module for assessment of environmental and health impacts MAEH. It has been designed as a web application employing computational resources of the ZEUS cluster of the PL-Grid infrastructure. The results show, that the impact of carbon prices on the fuel and technological power generation structure is much stronger as compared to impact of fuel prices. Future PM emissions from the centralized power and heat generation sector do not differ much irrespective of energy scenario considered. For analysed cases, the statistical life expectancy in Poland due to long-term exposure to PM_2.5 air pollution is reduced on average by approx. 183 days. That gives over 12 million years lost for all cohorts included in the analysis.     

Influences of urban forms on traffic-induced noise and air pollution: Results from a modelling system

Based on the work of Jensen [Jensen, S.S., 1998. Mapping human exposure to traffic air pollution using GIS. Journal of Hazardous Materials 61(1–3), 385–392; Jensen, S.S., 1999. A geographic approach to modelling human exposure to traffic air pollution using GIS. Ph.D. Thesis. National Environmental Research Institute, Roskilde], a prototype system for modelling noise and air pollution is developed for the Macao Peninsula. The system integrates a road traffic noise model, an operational air pollution model, digital maps, an urban landscape model and a Geographic Information System (GIS). Compared with mesoscale model systems with input/output resolution in kilometres, the present one has a higher spatial resolution down to individual buildings along both sides of the street. Applying the developed model system, a preliminary study investigates the ways that four urban forms existing nowadays on the Macao Peninsula influence vehicle transport and street environment. This paper shows that the urban forms in historical areas with narrower roads, complex road networks and a higher density of intersections lead to lower traffic volumes and thus lower noise pollution. However, the greater street canyon effects in these historical urban areas lead to higher carbon monoxide (CO) concentrations.     

Modelling PM2.5 impact indicators in Europe: Health effects and legal compliance

Long-term exposure to fine particulate matter (PM_2.5) has been shown to have significant negative impacts on human health. It is estimated that current levels of air pollution shorten the statistical life expectancy of European citizens by several months. The GAINS integrated assessment model calculates shortening of life expectancy from population exposure to PM_2.5 using epidemiologically-derived health impact functions. In addition, GAINS estimates PM_2.5 concentrations at 1875 air quality monitoring stations located in diverse environments ranging from remote background locations to busy street canyons. In this article, different approaches to dealing with the PM_2.5 pollution problem are compared. We assess for the present and future the attainment of EU and WHO air quality standards for PM_2.5 and estimate the loss of life expectancy under different policy scenarios developed for the ongoing revision of the EU Air Quality Legislation.     

Unraveling environmental justice in ambient PM2.5 exposure in Beijing: A big data approach

Air pollution imposes significant environmental and health risks worldwide and is expected to deteriorate in the coming decade as cities expand. Measuring population exposure to air pollution is crucial to quantifying risks to public health. In this work, we introduce a big data analytics framework to model residents' stay and commuters' travel exposure to outdoor PM_2.5 and evaluate their environmental justice, with Beijing as an example. Using mobile phone and census data, we first infer travel demand of the population to derive residents' stay activities in each analysis zone, and then focus on commuters and estimate their travel routes with a traffic assignment model. Based on air quality observations from monitoring stations and a spatial interpolation model, we estimate the outdoor PM_2.5 concentrations at a 500-m grid level and map them to road networks. We then estimate the travel exposure for each road segment by multiplying the PM_2.5 concentration and travel time spent on the road. By combining the estimated PM_2.5 exposure and housing price harnessed from online housing transaction platforms, we discover that in the winter, Beijing commuters with low wealth level are exposed to 13% more PM_2.5 per hour than those with high wealth level when staying at home, but exposed to less PM_2.5 by 5% when commuting the same distance (due to lighter traffic congestion in suburban areas). We also find that the residents from the southern suburbs of Beijing have both lower level of wealth and higher stay- and travel- exposure to PM_2.5, especially in the winter. These findings inform more equitable environmental mitigation policies for future sustainable development in Beijing. Finally, or the first time in the literature, we compare the results of exposure estimated from passive data with subjective measures of perceived air quality (PAQ) from a survey. The PAQ data was collected via a mobile-app. The comparison confirms consistencies in results and the advantages of the big data for air pollution exposure assessments.     

Bicycle messengers: energy expenditure and exposure to air pollution

The purpose of the study was to determine the level of energy expenditure and exposure to air pollution for bicycle messengers. Relationships between heart rate (HR) and oxygen uptake, and between HR and pulmonary ventilation (VE) for each participant were established in laboratory tests. Air pollution and HR were measured during one working day. The total oxygen uptake was then described as the total energy expenditure in Joule (J) and in multiples of the energy expenditure at rest (MET). The mean energy expenditure during a working day (8 h) was 12 MJ, (4.8 MET). The level of air pollution exposure when cycling seemed to be comparable with the levels of exposure when sitting inside a vehicle. The VE during cycling was four times higher than resting value. Increased VE led to increased exposure to air pollution.     

Bicycle messengers: energy expenditure and exposure to air pollution

The purpose of the study was to determine the level of energy expenditure and exposure to air pollution for bicycle messengers. Relationships between heart rate (HR) and oxygen uptake, and between HR and pulmonary ventilation (VE) for each participant were established in laboratory tests. Air pollution and HR were measured during one working day. The total oxygen uptake was then described as the total energy expenditure in Joule (J) and in multiples of the energy expenditure at rest (MET). The mean energy expenditure during a working day (8 h) was 12 MJ, (4.8 MET). The level of air pollution exposure when cycling seemed to be comparable with the levels of exposure when sitting inside a vehicle. The VE during cycling was four times higher than resting value. Increased VE led to increased exposure to air pollution.     

Estimation of air pollutant concentrations by the Galerkin finite element method

This paper is concerned with the estimation of air pollutant concentrations by solving the advection-diffusion equation using the Galerkin finite element method. The physical properties denoting the transport arid diffusion of the air pollution are assumed to be expressed by the two-dimensional advection-diffusion equation where the height of the stack is low and the vertical diffusion is small. The distributions of wind velocities and background concentrations are estimated using an interpolation method based on the observed data at monitoring stations. The Galerkin finite element method estimates the temporal and spatial distributions of air pollutant concentrations by solving the advection-diffusion equation. The estimation experiment of sulphur dioxide concentrations is carried out over the industrial area of Tokushima Prefecture, Japan, and the numerical results indicate the effectiveness of the proposed, method.     

An Internet of Things Platform for Air Station Remote Sensing and Smart Monitoring

Air pollution is currently receiving more attention by international governments and organizations. Nevertheless, current systems for air quality monitoring lack essential requirements which are key in order to be effective concerning users’ access to the information and efficient regarding real-time monitoring and notification. This paper presents an Internet of Things platform for air station remote sensing and smart monitoring that combines Big Data and Cloud Computing paradigms to process and correlate air pollutant concentrations coming from multiple remote stations, as well as to trigger automatic and personalized alerts when a health risk for their particular context is detected. This platform has been tested by analyzing the results of observing Andalusian, South of Spain, sensor network during a long period of time. The results show that this novel solution can help to reduce the impact of air pollution on human health since citizens are alerted in real time.     

Scale effects in remotely sensed greenspace metrics and how to mitigate them for environmental health exposure assessment

Metrics representing exposure to the natural environment are widely used in environmental health-related studies. They are calculated using a variety of different data sources representing greenspace and a range of buffer sizes representing human interaction with the environment. Previous studies have identified issues relating to buffer distance and scaling effects on greenspace exposure assessments when using satellite image-derived metrics. We evaluate the spatial scale sensitivity of three common greenspace metrics (i.e., Normalised Difference Vegetation Index- NDVI, Leaf Area Index- LAI, and Land Use and Land Cover-LULC), using lacunarity analysis, as a scale-dependent measure of heterogeneity based on the principles of fractals. By producing a ‘lacunarity curve’ across multiple spatial scales, we defined the scale-variances for specific greenspace metrics, including the upper scale limit at which the metrics become invariant, approximately 640 m for Sentinel-2 and 480 m for Landsat-8. Each of the greenspace metrics we considered exhibited scale sensitivities, meaning that each is expected to have a different influence on the strength and significance of the statistical associations found between greenspace exposure and health depending on the spatial scale of analysis (e.g., buffer distance). Using lacunarity curves, we produced a novel composite, multi-scale greenspace ‘exposure index’ in which each input scale is weighted according to its relative scale sensitivity. We also created a multi-scale, multi-metric map combining the different vegetation measures while accounting for scale. We found that cumulative exposure gradients across a large urban conurbation are even more marked when using our multi-scale ‘exposure index’ maps compared to traditional approaches. Our multi-scale, composite greenspace ‘exposure index’ mapping techniques are not as vulnerable to scale effects as traditional approaches and can be readily transferred to the analysis of other environmental exposure variables such as air pollution.     

An overview of air quality analysis by big data techniques: Monitoring,forecasting, and traceability

With the rapid development of economy and the frequent occurrence of air pollution incidents, the problem of air pollution has become a hot issue of concern to the whole people. The air quality big data is generally characterized by multi-source heterogeneity, dynamic mutability, and spatial–temporal correlation, which usually uses big data technology for air quality analysis after data fusion. In recent years, various models and algorithms using big data techniques have been proposed. To summarize these methodologies of air quality study, in this paper, we first classify air quality monitoring by big data techniques into three categories, consisting of the spatial model, temporal model and spatial–temporal model. Second, we summarize the typical methods by big data techniques that are needed in air quality forecasting into three folds, which are statistical forecasting model, deep neural network model, and hybrid model, presenting representative scenarios in some folds. Third, we analyze and compare some representative air pollution traceability methods in detail, classifying them into two categories: traditional model combined with big data techniques and data-driven model. Finally, we provide an outlook on the future of air quality analysis with some promising and challenging ideas.     

Collaborative virtual geographic environments: A case study of air pollution simulation

The integration of high dimensional geo-visualization, geo-data management, geo-process modeling and computation, geospatial analysis, and geo-collaboration is a trend in GIScience. The technical platform that matches the trend forms a new framework unlike that of GIS and is conceptualized in this paper as a collaborative virtual geographic environment (CVGE). This paper focuses on two key issues. One is scientific research on CVGE including the concept definition and the conceptual and system framework development. The other is a prototype system development according to CVGE frameworks for air pollution simulation in the Pearl River Delta. The prototype system integrates air pollution source data, air pollution dispersion models, air pollution distribution/dispersion visualization in geographically referenced environments, geospatial analysis, and geo-collaboration. Using the prototype system, participants from geographically distributed locations can join in the shared virtual geographic environment to conduct collaborative simulation of air pollution dispersion. The collaborations supporting this simulation happen on air pollution source editing, air pollution dispersion modeling, geo-visualization of the output of the modeling, and geo-analysis.     

Air pollution forecasting based on attention-based LSTM neural network and ensemble learning

With air pollution having become a global concern, scientists are committed to working on its amelioration. In the field of air pollution prediction, there have been good results in experimental research so far, but few studies have integrated weather forecast information and the properties of air pollution drift. In this work, we propose a novel wind-sensitive attention mechanism with a long short-term memory (LSTM) neural network model to predict the air pollution - PM2.5 concentrations by considering the influence of wind direction and speed on the changes of spatial–temporal PM2.5 concentrations in neighbouring areas. Preliminary predictions for PM2.5 are then made by an LSTM neural network regarding neighbouring pollution; these predictions are “paid attention to” and we finally apply an ensemble learning method based on e X treme G radient B oosting (XGBoost) to combine the preliminary predictions with weather forecasting to make second phase predictions of PM2.5. The experiment is conducted using PM2.5 data and weather forecast data. Our results illustrate that the proposed method is superior to other methods in predicting PM2.5 concentrations, including multi-layer perceptron, support vector regression, LSTM neural network, and extreme gradient boosting algorithm.     

Athenian photochemical smog: intercomparison of simulations (APSIS), background and objectives

Athens is suffering under severe photochemical air pollution levels. The spatial variation of the photosmog characteristics in the Athens basin reveals that air pollution levels in Athens are largely affected by local wind circulation systems. A complete picture of wind field pattern and air pollutant dispersion in Athens can only be achieved with suitable mathematical models. In view of its complexity and its manifold peculiarities, the situation in Athens qualifies as a test case for prognostic mesoscale models and photochemical dispersion models. As the frame of model intercomparisons and of attempts to evaluate model simulation results, the APSIS project was initiated. With the participation of more than 30 scientific teams from four continents, this project is expected to contribute to the refinement of atmospheric environmental software.     

基于ResGCN-GRU的大气污染风险源识别

重污染天气是“十四五”时期大气污染治理的重点工作,在重污染天气时期对风险源进行精准识别,可以及时发出预警,做好环境污染治理,防止污染事件进一步加重.基于网格化监测技术获取的数据,本文提出一种结合残差网络(ResNet)、图卷积网络(GCN)和门控循环网络(GRU)的深度学习模型ResGCN-GRU,该模型主要应用于重污染天气时期识别风险源.重污染天气的风险源往往都是区域性的,具有明显的时空特征,因而本文先利用GCN网络提取监测点位之间的空间特征,同时利用ResNet解决多层GCN带来的过平滑以及梯度消失问题;再利用GRU提取风险源的时间特征,最后将全连接层融合的时空特征输入到Softmax激活函数得到二分类概率值,再根据概率值得到分类结果.为验证本文提出的模型性能,本文基于沈阳市72个监测点位的数据,通过精确度、召回率以及综合评价指标对GCN、LSTM、GRU和GCN-GRU进行对比,实验结果表明ResGCN-GRU模型分类效果的精确度分别要好16.9%、4.3%、3.1%、2.9%,证明了本文提出的模型在大气风险源识别方面更加有效,可以根据风险源数据的时空特征达到对风险源的精准识别.     

水污染物扩散模型三维可视化的关键技术

在分析污染物扩散模型的基础上,针对模型的大规模数据难以可视化的缺点,提出水污染物扩散模型三维可视化的关键技术,其中包括三维地形组织与管理、河道数据预处理、污染物浓度等级划分、污染物时空分布等关键技术。实验测试结果证明,该系统具有较低的时间、空间开销,适合水污染物模型真三维的可视化。     

Simultaneous retrieval of aerosol optical properties over the Pearl River Delta, China using multi-angular, multi-spectral, and polarized measurements

The optical properties of aerosol have been simultaneously retrieved over the Pearl River Delta (PRD), China during December 2009 from multi-angular, multi-spectral, and polarized airborne data. A new airborne Directional Polarimetric Camera (DPC) with high spatial resolution (4 m at 4000 m a.g.l.) is used to retrieve the aerosol optical properties, which is an experimental airborne instrument focused on monitoring aerosol particle pollution, dealing with the apportionment of sources and controlling air quality in cities. We present a case study of polarized observations performed during high air pollution episodes in the southeast of China. Exceptionally high values of the aerosol optical depth of up to 0.8 (at 0.865 μm) were observed in this case study. The spatial and temporal variability of aerosol optical properties over the Pearl River Delta region were analyzed using polarized measurements with high spatial resolution. To reduce the ambiguity in retrieving aerosol optical properties using DPC alone, ground-based measurements (Automatic CE318-DP polarized sun-sky radiometer, Raman Lidar) were used to constrain the inversion in terms of the key characteristics of a local aerosol model, including spectral complex refractive index, size distribution, and vertical distribution of aerosol optical parameters. The surface contribution to the polarized radiance was simulated using bidirectional polarized reflectance distribution function (BPDF), which was adjusted using DPC measurements at low altitude. The aerosol optical properties were retrieved using DPC over the Pearl River Delta, and are in good agreement with coincident sun photometer retrievals. The retrieval algorithm of aerosol optical properties using high spatial resolution polarized measurements proposed in this paper shows potential to retrieve the aerosol optical properties over cities.