Land Use Changes in Regional Economic Theory

Land use and land cover changes are a major source of environmental degradation and therefore a serious issue in sustainable development studies and in the integrated assessment of environmental problems. In an attempt to understand part of the complexity of land use change we here aim to explore the ways in which economists deals with the land use issue. We argue that space is one of the forgotten items in economics. Economists often seem to ignore high-resolution spatial dynamics either because they are not considered as an important feature of the problem or out of habit. It seems as though there is a trade-off between spatial resolution and human behaviour in current applications. Certain types of models are capable of capturing the spatial complexity of urban and regional areas, for instance, by using cellular automata. Other types of land use models clearly have shortcomings where it concerns spatial detail. Dynamic modelling approaches do not guarantee a high spatial resolution.We propose to give more attention to agent based modelling as this type of modelling provides a specific connection between processes on micro level and macro-level spatial structures.     

Using of high-resolution topsoil magnetic screening for assessment of dust deposition: comparison of forest and arable soil datasets

Magnetic susceptibility (κ) is an easily detectable geophysical parameter that can be used as a proxy or semi-quantitative tracer of atmospheric industrial and urban dusts deposited in topsoil. An enhanced κ value of topsoil is in many cases also associated with high concentrations of soil pollutants (mostly heavy metals). High-resolution magnetic screening of topsoil in areas of high pollution influx is a useful tool for detection of pollution “hot spots”. General and regional screening maps with a grid density of 10 or 5 km have been performed on the basis of forest topsoil measurement only. The purpose of this study was to perform high-resolution magnetic screening with different grid densities in both forested and agricultural areas (arable land). Our large study area (ca. 200 km²) was located in a relatively more polluted region of the central part of Upper Silesia, and a second (small) one (ca. 100 m²) was located in the western part of Upper Silesia, with considerably lower influx of pollution. In the framework of this study, we applied a statistical comparison of data obtained in forested areas and on arable land. The arable soil showed statistically significantly lower κ values, the result of “physical dilution” of the arable layer caused by annual ploughing. Thus arable soils must be avoided during high-resolution field measurement. From semivariograms, it was clear that the spatial correlations in forest topsoil are much stronger than in arable soil, which suggests that a denser measurement grid is required in forested areas.     

Evaluation of the best compromise between the urban air quality monitoring resolution by diffusive sampling and resource requirements

The need to collect data representative of overall urban pollution is all-important in order to monitor the population exposure. High spatial resolution monitoring using diffusive samplers allows studying of the urban pollutant distribution, thus enabling deeper investigation of their generation and diffusion mechanisms. Nevertheless, such a monitoring campaign has a certain cost. In this study we point out how to find the best compromise between the number of necessary measurements and the affordable costs for monitoring campaigns. We also describe an innovative method for the proper design of a fixed urban monitoring network by means of preliminary high spatial resolution campaigns using diffusive samplers. Four European capital cities (Dublin, Madrid, Paris and Rome) were monitored six times, each time for seven days. Benzene, toluene, ethylbenzene, xylenes (BTEX) and NO(2) concentrations were measured at 146 sites in Dublin, 293 in Madrid, 339 in Paris and 290 in Rome. Multiscale grids have been drawn which ranged in mesh size from 500 m to 2 km. The statistical processing of data produced a twofold result: the creation of isoconcentration maps with geostatistical procedures, and an algorithm aimed at locating the minimum number of sampling sites where the fixed monitoring stations should be placed. Average urban levels estimated on the basis of these selected sites differ by less than 8% from those calculated on the whole populations of the sampled points. The aim of this work is to investigate how far the resolution of a monitoring campaign of urban pollution by diffusive sampling can be reduced, thus making the monitoring less expensive in terms of human and financial resources, while preserving the same quality of the results that could be achieved with a higher resolution. We found that there is no significant loss of information when the resolution of the monitoring grid for BTEX is lowered to a mesh size of 1.85 km, that is a sampling site each 3.4 km(2), and that the minimum number of sampling sites to be used is N = 0.29 A, where A is the urban surface to be monitored (in km(2)). As the spatial distribution of NO(2) is less sensitive to the distance from the emission source than that of BTEX, this relationship could be retained as a valid lower limit for the mesh grid size also for NO(2) monitoring.     

Emission database for global atmospheric research (Edgar)

Atmospheric chemistry and climate modellers require gridded global emissions data as input into their models. To meet this urgent need a global emissions source database called EDGAR is being developed by TNO and RIVM to estimate for 1990, on a regional and on a grid basis, annual emissions of greenhouse gases (CO₂, CH₄, N₂O, CO, NO_x, non-methane VOC, SO_x), of NH₃, and of ozone depleting compounds (halocarbons) from all known sources. The aim is to establish at due levels of spatial, temporal and source aggregation the emissions from both anthropogenic and biogenic sources: a complete set of data required to estimate the total source strength of the various gases with a 1×1 ° resolution (altitude resolution of 1 km) and a temporal resolution of a month, supplemented by diurnal variation, as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC). In this way EDGAR will meet the requirements of present and future developments in the field of atmospheric modelling. The data comprise demographic data, social and economic factors, land use distributions and emission factors (with due emphasis on the uncertainty). As understanding in this field is still changing, due attention is paid to flexibility regarding the disaggregation of sources, spatial and temporal resolution and species. The objective and methodology chosen for the construction of the database and the structural design of the database system are presented, as well as the type and sources of data and the approach used for data collection. As an example, the construction of the N₂O inventory is discussed.     

Forecasting Air Quality Parameters Using Hybrid Neural Network Modelling

Urban air pollution has emerged as an acute problem in recent years because of its detrimental effects on health and living conditions. The research presented here aims at attaining a better understanding of phenomena associated with atmospheric pollution, and in particular with aerosol particles. The specific goal was to develop a form of air quality modelling which can forecast urban air quality for the next day using airborne pollutant, meteorological and timing variables.Hourly airborne pollutant and meteorological averages collected during the years 1995–1997 were analysed in order to identify air quality episodes having typical and the most probable combinations of air pollutant and meteorological variables. This modelling was done using the Self-Organising Map (SOM) algorithm, Sammon's mapping and fuzzy distance metrics. The clusters of data that were found were characterised by statistics. Several overlapping Multi-Layer Perceptron (MLP) models were then applied to the clustered data, each of which represented one pollution episode. The actual levels for individual pollutants could then be calculated using a combination of the MLP models which were appropriate in that situation.The analysis phase of the modelling gave clear and intuitive results regarding air quality in the area where the data had been collected. The resulting forecast showed that the modelling of gaseous pollutants is more reliable than that of the particles.     

The Spatial-Scale Effect of an Atmospheric Environmental Impact Assessment in Regional Strategic Environmental Assessment (R-SEA)

When evaluating the atmospheric environment in regional strategic environment assessment (R-SEA), the variation and choice of the spatial scale have a substantial influence on the conclusions of the assessment. In this study, we used numerical simulation to investigate the spatial-scale effect. Two varying spatial extents and two varying spatial details of pollutant emission data (emission inventories in this case) were provided for numerical modeling, and output distributions of atmospheric pollutants at different air pollution levels were compared. The results show that the resolution and spatial range of data collection do indeed influence the atmospheric prediction and assessment results in R-SEA. The spatial-scale effect is more significant under the air pollution condition than under excellent and good air quality conditions. A comparison of varying spatial extents of emission inventory shows that narrowing the prediction area to a local scale is more conducive to identifying the impact of local pollution sources. A comparison of varying spatial details of emission inventory indicates that a higher resolution is favorable for identifying local high concentrations of pollutants and their locations.     

Retrieval And Monitoring of Atmospheric Trace Gas Concentrations in Nadir and Limb Geometry Using the Space-Borne Sciamachy Instrument

The Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) onboard the European Envisat spacecraft performs continuous spectral observations of reflected, scattered and transmitted sunlight in various observation geometries. A unique feature of SCIAMACHY is the capability of probing the atmosphere in three different observation geometries:The nadir, limb, and occultation measurement modes. In nadir mode, column densities of trace gases are retrieved with a spatial resolution of typically 30× 60 km using the Differential Optical Absorption Spectroscopy (DOAS) technique (Platt and Perner, 1983). Alternating with the nadir measurement, vertical profiles of absorber concentration in the stratosphere are derived in limb and occultation. In this paper we present an overview over some applications of SCIAMACHY data in space-based monitoring of atmospheric pollution. The DOAS algorithms for the retrieval of total column amounts from nadir spectra are briefly described and case studies of pollution events are presented. We also illustrate the technique used to derive stratospheric concentration profiles from limb observations and show comparisons with other remote sensing systems. Special emphasis will be given to techniques, which take advantage of SCIAMACHY's different viewing geometries. In particular, we will discuss the potential and limits of strategies to infer tropospheric abundances of O₃and NO₂.     

Air-quality Prognosis, for the Implementation of Abatement Strategies over Large Urban Areas

State-of-the-art approaches for urban air-quality characterisation have several drawbacks due to apriori assumptions and/or due to inherent limitations of the concept utilised. For the evaluation of abatement scenarios it is either necessary to embark on extensive monitoring campaigns or to consistently apply numerical models for atmospheric dispersion. The 'ENVISOR' methodology applied here is a mixture of the two approaches. It forecasts pollutant concentrations during real episodes and assesses the impact from the construction of a new highway across a large urban domain of 100×100 km2. Data from an extensive monitoring network are used to identify real modelling periods and for validating the modelling simulations. The selected periods are aiming to the assessment of 'annual mean' or 'episodic' conditions. These periods are short-listed according to the abatement scenario under consideration. This approach yields accurate forecasts for the concentration of pollutants after extensive validation tests extended over the whole domain. It is foreseen that the impact from the highway construction will be minimal for photochemical pollution whereas, higher impact will result for inert pollutants due to additional emissions from the highway.     

Air pollution exposure monitoring and estimating. Part I. Integrated air quality monitoring system

This paper presents an integrated exposure monitoring system, based on an expansion of existing air quality monitoring systems using dispersion modelling. The system allows: (1) identifying geographical areas whose inhabitants are most exposed to ambient pollution; (2) identifying how many people in an area are exposed to concentrations of pollution exceeding air quality guidelines; (3) describing the exposure of population subgroups (e.g. children); (4) planning pollution abatement measures and quantifying their effects; (5) establishing risk assessment and management programs, and (6) investigating the short- and long-term effects of both pollutants and pollution sources on public health. The effect of pollution is rarely very large and in order to discover it, exposure estimation must provide data that reflects both spatial and temporal variations. Estimates of pollution exposure are obtained using an integrated approach that combines results of measurements from monitoring programs with dispersion calculations. These values can serve as estimates for individual short-term or long-term exposure. The grouped data allows the expression of ambient pollution concentrations as the spatial distribution of estimates such as the mean or 98th percentile of such compounds as SO2, O3, NO2, PM10 and PM2.5. This integrated approach has been combined into a single software package, AirQUIS.     

大气环境高灵敏光谱探测技术

大气环境高灵敏光谱探测技术是以光与环境相互作用为基础发展起来的学科交叉方向,使用光学的方法和手段来探知研究环境问题。大气环境高灵敏光谱探测技术能够实现从现场瞬态灵敏探测到污染时空分布等不同尺度的遥测,从而获得大气污染"点-线-面"的时空变化规律,由于光谱探测技术具有多组分、非接触、无采样、高灵敏度、大范围快速以及遥测等特点,已经成为国际上大气环境监测技术的主要方向之一。大气环境高灵敏光谱技术在污染源、工业园区、空气质量以及区域污染等监测方面都有很广泛的应用。针对化工园区复杂的环境污染问题,光谱探测技术可以高灵敏、非接触地获取多种污染物浓度特征,掌握化工园区污染时空分布状况,为化工园区的大气污染治理提供有效支撑;针对京津冀、长三角等地区大气重污染过程的核心问题,大气环境高灵敏光谱探测技术通过地基组网观测,车载/机载平台移动观测等三维立体监测手段获取重点区域污染物的生成、转化过程以及重污染过程的形成机制,为区域大气污染成因追溯、污染控制措施评估提供科学的数据和技术支撑。高灵敏光谱探测技术在大气环境领域的成功应用,为污染源、化工园区和重点区域的环境综合诊断和治理奠定了技术基础。     

Aggregated Versus Individual Land-Use Models: Modeling Spatial Autocorrelation to Increase Predictive Accuracy

The objective of this paper is to compare the predictive accuracy of individual and aggregated econometric models of land-use choices. We argue that modeling spatial autocorrelation is a comparative advantage of aggregated models due to the smaller number of observation and the linearity of the outcome. The question is whether modeling spatial autocorrelation in aggregated models is able to provide better predictions than individual ones. We consider a complete partition of space with four land-use classes: arable, pasture, forest, and urban. We estimate and compare the predictive accuracies of individual models at the plot level (514,074 observations) and of aggregated models at a regular 12 × 12 km grid level (3,767 observations). Our results show that modeling spatial autocorrelation allows to obtain more accurate predictions at the aggregated level when the appropriate predictors are used.     

传感器技术在环境空气监测与污染治理中的应用现状、问题与展望

大气环境污染物监测是建立污染源与环境污染关系的重要技术途径,也是大气综合治理决策所需数据的主要来源。近年来,空气质量精细化管理和污染源精准控制的需求逐渐增长,对监测技术提出了更高的要求。传感器技术作为空气质量监测标准方法的补充,是实现城市空气质量高时空分辨率测量的新手段。研究从传感器的原理及分类入手,通过综述其在环境空气质量监测、污染源监控管理、个人暴露风险评估等领域的应用,总结了传感器技术的研发进展、应用现状和存在问题,并提出其发展方向和前景。     

Simulation of Wind Circulation and Pollutant Diffusion Over the Pearl River Delta Region

Changes in urban surface areas and population growth have significantly affected the weather and environment. Emissions of nitrogen oxides are increasing in the Pearl River Delta region. Nitrogen compounds emitted by factories and motor vehicles are the major sources of nitric pollution. To study the impacts of urbanization and the relationship between pollutant diffusion and the atmospheric environment, the nonhydrostatic mesoscale forecast model MM5 (v3.73), which was developed by Penn State University and the National Center of Atmospheric Research, and a mass continuity equation for air pollutants, were used in this study. Two experiments were designed. One experiment (BE) applied horizontal grid resolutions of 27, 9, 3, and 1?km in four nested domains. The other experiment adopted new land-use data (in domain 4) directly retrieved from Landsat Thematic Mapper imagery to replace the 1980s data of the United States Geological Survey in BE. A 48-h simulation (from 0000?UTC on 21 October to 0000?UTC on 23 October 2008) was conducted, with the first 12?h being the spin-up time and the remaining 36?h being the effective simulation, so as to capture the diurnal features of the thermally induced winds associated with the land–sea breeze and urban heat island circulations. The different results obtained from the two tests for wind circulation and air pollution dispersion and transportation in the Pearl River Delta region were analyzed. The simulated results show that the both experiments can well simulate land–sea breeze circulation and remarkable land–sea breeze evolution, comparing with observation data. The height of the PBL had a significant diurnal cycle. The structure of the wind field can obviously impact the dispersion of the NO _x in three dimensions. Nitrogen oxides mainly diffused along the dominant wind direction (east or southeast wind), therefore the majority of the pollutants accumulated in the northwest region of the fine domain in both simulation experiments. However, it induced the pollutants concentration in an irregular pattern due to the fine-resolution grid spaces and complicated inland wind field in the northwest area of the inner domain. Moreover, increasing the proportion of urban surface caused sensible heat flux increase, latent heat flux decrease and humility reducing relatively in the region of urban surface characteristics apparently. Urbanization will cause pollution accumulated severely over the urban surface.     

基于激光雷达走航车的大气污染物探测技术及其应用

大气气溶胶对人体健康环境和全球气候都有一定的影响,是大气污染的主要来源。为了提升空气质量,大气污染防治刻不容缓。把气溶胶探测激光雷达和测风激光雷达集成在车辆上,进行走航和扫描探测可获得立体的大气气溶胶时空变化状态图。以合肥和芜湖等地大气气溶胶探测为例,基于激光雷达走航车的探测数据,获得大气气溶胶消光系数时空分布图,搜寻污染物排放源和估算颗粒物的PM_2.5输送通量。由此可知,激光雷达走航式探测技术是获得大气污染立体图像便捷的、有效的方法,可为大气污染防治提供精准的科学依据,具有重要的应用价值。     

Application of CFD Methods for Modelling in Air Pollution Problems: Possibilities and Gaps

Different urban air pollution problems deal with complex structure of air flows and turbulence. For such problems the Computer Fluid Dynamics (CFD) methods become widely used. However, this approach despite a number of advantages has some problems. Experience of use of CFD tools for development of models and suggestions of their applications for a local scale air pollution over a complex terrain and stable stratification are discussed in this paper, including: Topography and complex geometry: choose of the co-ordinate system and computer grid; Turbulence closure for air pollution modelling: modified k- model for stable stratified ABL; Boundary conditions for vertical profiles of velocity for stable-stratified atmosphere; Effects of the radiation and thermal budget of inclined surfaces to dispersion of pollutants; Artificial sources of air dynamics and circulation.Some examples of CFD applications for air pollution modelling for a flat terrain, mountainous area, mining open cast and indoor ventilation are discussed. Modified k- model for stably-stratified ABL is suggested. Due to the isotropic character of the k- model a combination of it in vertical with the sub-grid turbulence closure in horizontal can be more suitable for ABL. An effective scheme of boundary conditions for velocity profiles, based on the developed similarity theory for stable-stratified ABL, is suggested. Alongside with the common studies of atmospheric dispersion, the CFD methods have also demonstrated a good potential for studying anthropogenic and artificial-ventilation sources of air dynamic and circulation in local-scale processes of air pollution.     

Urban Air Pollution Patterns, Land Use, and Thermal Landscape: An Examination of the Linkage Using GIS

This article investigates the relationship of local air pollution pattern with urban land use and with urban thermal landscape using a GIS approach. Ambient air quality measurements for sulfur dioxide, nitrogen oxide, carbon monoxide, total suspended particles, and dust level were obtained for Guangzhou City in South China between 1981 and 2000. Landsat TM images and aerial photo derived maps were used to examine city's land use and land cover at different times and changes. Landsat thermal infrared data were employed to compute land surface temperatures and to assess urban thermal patterns. Relationships among the spatial patterns of air pollution, land use, and thermal landscape were sought through GIS and correlation analyses. Results show that the spatial patterns of air pollutants probed were positively correlated with urban built-up density, and with satellite derived land surface temperature values, particularly with measurements taken during the summer. It is suggested that further studies investigate the mechanisms of this linkage, and that remote sensing of air pollution delves into how the energy interacts with the atmosphere and the environment and how sensors see pollutants. Thermal infrared imagery could play a unique role in monitoring and modeling atmospheric pollution.     

Multipoint source method in air pollution modelling of cold start emission

The paper presents a new method of air pollution modelling on a micro scale. For estimation of concentration of car exhaust pollutants, each car is treated as an instantaneous moving emission source. This approach enables us to model time and spatial changes of emission, especially during cold and cool start of an engine. These stages of engine work are a source of significant pollution concentration in urban areas. In this work, two models are proposed: one for the estimation of emission after cold start of the engine and another for the prediction of pollutant concentration. The first model (defined for individual exhaust gas pollutants) enables us to calculate the emission as a function of time after the cold or cool start, ambient temperature and average speed of motion. This model uses the HBEFA database. The second mathematical model is developed in order to calculate the pollutant dispersion and concentrations. The finite volume method is applied to discretise the set of partial differential equations describing wind flow and pollutant dispersion in the domain considered. Models presented in this paper can be called short-term models on a small spatial scale. The results of numerical simulation of pollutant emission and dispersion are also presented.     

唐山市钢铁行业大气污染物排放清单建立

以唐山市钢铁企业为研究对象,在收集已有排放源数据的基础上,基于排放因子和活动水平数据采用排放因子法估算了唐山市钢铁企业多个大气污染物的排放量,得到了符合空气质量模型要求的污染源输入数据,建立钢铁行业和主要防控因子污染源数据库。结合唐山市各县区的环境空气质量状况,利用GIS技术,将不同污染因子的排放进行空间分布,最终形成准确完善的多尺度、高时空分辨率大气污染源排放清单。研究介绍了符合中国特色的区域高分辨率大气排放源清单建立的方法体系,为京津冀地区区域大气污染联防联控及2020年大气污染物区域削减计划工作提供数据支撑。     

烟尘由浓度控制向总量控制的初步探讨

煤烟型污染是我国我省也是我市大气污染的重要因素。控制烟尘污染源的排放浓度，虽对减轻污染，促进治理起了很大作用，但不能防止污染点源密集、大气环境容量严重超出的地区不可避免造成污染负荷增加；不能促使大气环境质量有明显改善。本文对日照市城区的烟尘污染源排放由浓度控制向总量控制过渡进行初步探讨。