兰州新区大气环境容量特征

通过选取兰州中川机场2008-2010年的地面气象观测资料,利用箱模型,在[WTBX]A[WTBZ]值法的基础上,加入了环境变动量,对规划中的兰州新区大气环境容量的年际变化、年变化、日变化等进行了分析。该模型在计算大气环境容量时体现了污染因子在大气环境中的迁移转化规律, 能够反映污染物在大气环境中的动态变化过程。结果得到兰州新区SO₂全年大气环境容量为15.03×10⁴ t,PM^₁₀为18.03×10₄ t,NO₂为15.16×10⁴ t。基本揭示了该地区大气环境容量的特征,将对新区规划建设及污染物排放限额制定起到了一定的指导作用。     

Urban Form,Air Pollution,and CO2 Emissions in Large U.S. Metropolitan Areas

In this article we explore the relationships between urban form and air pollution among 86 U.S. metropolitan areas. Urban form was quantified using preexisting sprawl indexes and spatial metrics applied to remotely sensed land cover data. Air pollution data included the nonpoint source emission of the ozone (O₃) precursors nitrogen oxides (NOx) and volatile organic compounds (VOCs), the concentration of O₃, the concentration and nonpoint source emission of fine particulate matter (PM2.5), and the emission of carbon dioxide (CO₂) from on-road sources. Metropolitan areas that exhibited higher levels of urban sprawl, or sprawl-like urban morphologies, generally exhibited higher concentrations and emissions of air pollution and CO₂ when controlling for population, land area, and climate.     

Spatio-temporal modeling of PM2.5 concentrations with missing data problem: a case study in Beijing,China

ABSTRACT

One of the major challenges in conducting epidemiological studies of air pollution and health is the difficulty of estimating the degree of exposure accurately. Fine particulate matter (PM_2.5) concentrations vary in space and time, which are difficult to estimate in rural, suburban and smaller urban areas due to the sparsity of the ground monitoring network. Satellite retrieved aerosol optical depth (AOD) has been increasingly used as a proxy of ground PM_2.5 observations, although it suffers from non-trivial missing data problems. To address these issues, we developed a multi-stage statistical model in which daily PM_2.5 concentrations can be obtained with complete spatial coverage. The model consists of three stages – an inverse probability weighting scheme to correct non-random missing patterns of AOD values, a spatio-temporal linear mixed effect model to account for the spatially and temporally varying PM_2.5-AOD relationships, and a gap-filling model based on the integrated nested Laplace approximation-stochastic partial differential equations (INLA-SPDE). Good model performance was achieved from out-of-sample validation as shown in R² of 0.93 and root mean square error of 9.64 μg/m³. The results indicated that the multi-stage PM_2.5 prediction model proposed in the present study yielded highly accurate predictions, while gaining computational efficiency from the INLA-SPDE.     

The evaluation of PM10, PM2.5, and PM1 concentrations during the Middle Eastern Dust (MED) events in Ahvaz,Iran, from april through september 2010

In this study, PM₁₀, PM_2.5, and PM₁ concentrations were measured from April through September 2010. These measurements were made every six days and on days with dust events using a Grimm Model 1.177 aerosol spectrometer. Meteorological data were also collected. Overall mean values of 319.6 ± 407.07, 69.5 ± 83.2, and 37.02 ± 34.9 μg/m³ were obtained for PM₁₀, PM_2.5, and PM₁, respectively, with corresponding maximum values of 5337.6, 910.9, and 495 μg/m³. The presence of the westerly prevailing wind implied that Iraq is the major source of dust events in this area. A total of 72 dust days and 711 dust hours occurred in the study area. The dust events occurred primarily during July. The longest dust event during the study period occurred in July, lasted five days, and had a peak concentration of 2028 μg/m³. These high concentrations produced AQI values of up to 500. A total estimated mortality and morbidity of 1131 and 8157 cases, respectively, can be attributed to these concentrations. The results of this study indicated the importance of dust events in Ahvaz and their possible health impacts. The study also demonstrated the need to design and implement intergovernmental management schemes to effectively mitigate such events.     

Spatial-temporal characteristics of PM<Subscript>2.5</Subscript> in China: A city-level perspective analysis

Haze pollution has become a severe environmental problem in the daily life of the people in China. PM_2.5 makes a significant contribution to poor air quality. The spatio-temporal features of China’s PM_2.5 concentrations should be investigated. This paper, based on observed data from 945 newly located monitoring sites in 2014 and industrial working population data obtained from International Standard Industrial Classification (ISIC), reveals the spatio-temporal variations of PM_2.5 concentrations in China and the correlations among different industries. We tested the spatial autocorrelation of PM_2.5 concentrations in the cities of China with the spatial autocorrelation model. A correlation coefficient to examine the correlativity of PM_2.5 concentrations and 23 characteristic variables for 190 cities in China in 2014, from which the most important ones were chosen, and then a regression model was built to further reveal the social and economic factors affecting PM_2.5 concentrations. Results: (1) The Hu Huanyong Line and the Yangtze River were the E-W divide and S-N divide between high and low values of China. (2) The PM_2.5 concentrations shows great seasonal variation, which is high in autumn and winter but low in spring and summer. The monthly average shows a U-shaped pattern, and daily average presents a periodic and impulse-shaped change. (3) PM_2.5 concentrations had a distinct characteristic of spatial agglomeration. The North China Plain was the predominant region of agglomeration, and the southeastern coastal area had stable good air quality.     

基于空间计量模型的中国城市化发展与城市空气质量关系

采用中国289个地级及以上城市2016年的空气质量指数（AQI）和夜间灯光数据,运用空间滞后模型,从空间溢出效应视角出发探究了城市化发展与空气质量之间的定量关系。从空间滞后模型的估计结果来看,城市空气污染存在显著的空间溢出效应,周边地区空气质量的下降会导致本地空气污染情况加重。在城市发展过程中,城市经济发展水平的提高,引致的城市建设用地的扩张使得空气质量不断恶化;政府管制力的加强对空气污染的治理起到了促进作用,而居民环保意识和城市技术创新水平的提高有利于空气质量的改善。此外,PM2.5质量浓度的上升导致空气质量恶化。公路货运量在统计上与空气质量的关系不显著。从研究结果来看,协调城市发展与空气质量的关系以及加强空气污染防治的联防联控机制,是未来空气污染治理工作的重点。     

Spatial Correlation between Traffic and Air Pollution in Beijing

Many studies have attempted to model the sophisticated influence of traffic emissions on air pollution, but most models only calculate the contribution of traffic emissions near monitoring sites. It is difficult to observe the near-surface dynamics such as wind, rain, and human activities and precisely distinguish traffic emissions. These obstacles make model simulation very expensive in practice. The regional distribution patterns that can help adjust policies and actions taken remain unknown. Therefore, this article proposes a grid-oriented geostatistics-based approach to overcome these obstacles. We chose central Beijing as the study area. An experiment implemented the approach on data collected from Global Positioning System navigation software, car rental companies, and meteorology stations. The results suggest that the northwest area of Beijing has high traffic-related air pollution (TRAP) and the southeast area has low TRAP. Unlike modeling-based methods, this work uses geostatistical methods to directly study the spatiotemporal connections between traffic and PM_2.5 (particulate matter with diameter less than 2.5?μm) from the phenomenon. The calculation is conducted under no hypotheses and has little risk of producing results contradictory to facts. This work provides a reference for future TRAP research on directly learning from the phenomenon and assists decision makers with seamless spatiotemporal heat maps of TRAP distribution. Key Words: Beijing, geospatial statistics, PM_2.5, spatial correlation, traffic-related air pollution.     

外商直接投资对空气污染影响的空间异质性分析——以中国150个城市空气质量指数（AQI）为例

空气污染问题引起了人们极大的关注。以中国2014年150个地级市作为样本数据,采用空气质量指数（AQI）作为全面衡量空气污染的指标,运用地理加权回归模型从空间异质性视角出发,分析了不同城市外商直接投资与空气污染之间的关系。研究结果表明：总体上,外商直接投资由于带来了先进的技术,有利于空气质量的改善。此外,人均地区生产总值的增加、二氧化硫和PM_2.5浓度的提高均是导致空气污染加剧的重要因素;而环保意识的提升则有利于空气质量的改善。民用汽车保有量变量在统计上不显著。从地理加权回归模型估计结果来看,不同城市的外商直接投资对环境的改善作用存在显著的空间异质性。其中,东北城市群、关中城市群和长江中游城市群外商直接投资对空气质量的改善作用最大,山东半岛城市群和川渝城市群外商直接投资对空气质量的改善作用不明显。     

中国城市空气污染时空分布格局和人口暴露风险

近年来,中国空气污染及其对人口健康危害的时空分布呈现出新的特征。论文使用5 a(2015—2019年)间逐小时的空气质量监测数据,利用变化率计算、热点分析、趋势分析和超标频数统计等方法,分析了中国332个城市的空气质量及人口空气污染暴露风险的时空分布特征,结论如下：① 中国城市近年来空气质量有好转趋势,环境空气质量指数(AQI)下降的城市占所研究城市总数的91.3%;PM_2.5、PM₁₀、SO₂和CO等4种污染物浓度均有所下降,而NO₂和O₃浓度有所上升;② PM_2.5、PM₁₀、SO₂和CO浓度变化率的热点分布于新疆地区和云南—华南地区,NO₂浓度变化率的热点为新疆地区和河套平原,O₃浓度变化率的热点为华北平原至长江中下游流域;西北地区和华南地区空气质量变化幅度较小;③ 9个城市在PM_2.5、PM₁₀、SO₂、NO₂、O₃和CO等6种污染物中均有暴露,分布于山西、河北与山东。暴露风险均为0级的低风险城市共有12个,分别位于新疆、云南、贵州、四川、广东、福建和黑龙江。研究结论对于跨区域空气污染的协同治理以及制定差异化的空间人口流动管理政策具有重要参考价值。     

空气污染对城市活力的影响及其建成环境异质性——基于大数据的分析

建设充满活力的城市空间得到地理和城乡规划学者的广泛关注。随着空气污染问题的加剧,空气质量影响居民在城市空间中的活动,但鲜有研究考察空气污染与城市活力的定量关系。基于广州市2019年新浪微博签到记录、日气象和空气质量数据、以及建成环境数据,本研究构建以街道为空间单元、以天为时间单元的面板数据,通过标准差椭圆（SDE）以及面板回归模型测度空气污染对城市活力的抑制效应以及该抑制效应在不同建成环境上的异质性。研究得到以下结论：① 城市活力SDE面积随空气质量指数（AQI）上升而收缩,轻度污染和中度污染的城市活力SDE面积仅为空气质量优的约80%和30%。② 运用空间面板回归模型控制街道的空间关联性后,空气质量指数（AQI）对城市活力具有明显负向影响,AQI每增加1个单位,日活动强度减少约0.10次/10 km²;当空气质量恶化到中等污染后,AQI每增加1个单位,日活动强度减少约0.14次/10 km²。③ 空气污染对城市活力的抑制效应在不同建成环境上存在异质性,POI密度、离城市中心距离强化空气污染对城市活力的抑制效应,而地铁站密度、道路交叉口密度、土地利用混合度则弱化空气污染对城市活力的抑制效应。本研究有助于更好厘清空气污染、建成环境与城市活力的关系,并为优化建成环境以缓减空气污染对城市活力抑制效应提供分析支撑。