Mapping the spatio-temporal distribution of key vegetation cover properties in lowland river reaches,using digital photography

The presence of vegetation in stream ecosystems is highly dynamic in both space and time. A digital photography technique is developed to map aquatic vegetation cover at species level, which has a very high spatial and a flexible temporal resolution. A digital single-lens reflex (DSLR) camera mounted on a handheld telescopic pole is used. The low-altitude (5 m) orthogonal aerial images have a low spectral resolution (red-green-blue), high spatial resolution (～1.9 pixels cm^?2, ～1.3 cm length) and flexible temporal resolution (monthly). The method is successfully applied in two lowland rivers to quantify four key properties of vegetated rivers: vegetation cover, patch size distribution, biomass and hydraulic resistance. The main advantages are that the method is (i) suitable for continuous and discontinuous vegetation covers, (ii) of very high spatial and flexible temporal resolution, (iii) relatively fast compared to conventional ground survey methods, (iv) non-destructive and (v) relatively cheap and easy to use, and (vi) the software is widely available and similar open source alternatives exist. The study area should be less than 10 m wide, and the prevailing light conditions and water turbidity levels should be sufficient to look into the water. Further improvements of the image processing are expected in the automatic delineation and classification of the vegetation patches.     

A new approach for crop identification with wavelet variance and JM distance

This paper develops a new crop mapping method through combined utilization of both time and frequency information based on wavelet variance and Jeffries–Matusita (JM) distance (CIWJ for short). A two-dimensional wavelet spectrum was obtained from datasets of daily continuous vegetation indices through a continuous wavelet transform using the Mexican hat and the Morlet mother wavelets. The time-average wavelet variance (TAWV) and the scale-average wavelet variance (SAWV) were then calculated based on the wavelet spectrum of the Mexican hat and the Morlet wavelet, respectively. The class separability based on the JM distance was evaluated to discriminate the proper period or scale range applied. Finally, a procedure for criteria quantification was developed using the TAWV and SAWV as the major metrics, and the similarity between unclassified pixels and established land use/cover types was calculated. The proposed CIWJ method was applied to the middle Hexi Corridor in northwest China using 250-m 8-day composite moderate-resolution imaging spectroradiometer (MODIS) enhanced vegetation index (EVI) time series datasets in 2012. The CIWJ method was shown to be efficient in crop field mapping, with an overall accuracy of 83.6 % and kappa coefficient of 0.7009, assessed with 30 m Chinese Environmental Disaster Reduction Satellite (HJ-1)-derived data. Compared with methods utilizing information on either frequency or time, the CIWJ method demonstrates tremendous potential for efficient crop mapping and for further applications. This method could be applied to either coarse or high spatial resolution images for agricultural crop identification, as well as other more general or specific land use classifications.     

Landslide susceptibility mapping in an area of underground mining using the multicriteria decision analysis method

Landslides are geomorphological phenomena that affect anthropogenic and natural features on the Earth’s surface. Many previous studies have identified several factors that have contributed to landslides. Among these factors are physical characteristics, such as slope, aspect, and land cover, of Earth’s surface. Moreover, landslides can be triggered by human activities such as underground mining. This study aims to identify landslide susceptibility areas by analyzing landslide-related factors, including land subsidence triggered by underground mining. The area of interest was Kozlu, Turkey, where underground mining has been in progress for the past 100 years. Thus, to identify landslide risk zones, the multicriteria decision analysis method, together with the analytical hierarchy method, was used. The datasets included were topography, land cover, geological settings, and mining-induced land subsidence. The spatial extent of land subsidence was estimated using a previously published model. A landslide susceptibility map (LSM) was developed using a purposely developed GIS-based software. The results were compared with a terrain deformation map, which was developed in a separate study using the differential synthetic aperture radar interferometry (DInSAR) technique. The results showed a substantial correlation between the LSM and DInSAR map. Furthermore, it was found that ~?88% of the very high and high landslide risk areas coincided with location of the past landslide events. These facts suggest that the algorithm and data sources used were sufficient to produce a sufficiently accurate LSM, which may be used for various purposes such as urban planning.     

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。     

A new multiscale approach for monitoring vegetation using remote sensing-based indicators in laboratory, field, and landscape

Remote sensing is an important tool for studying patterns in surface processes on different spatiotemporal scales. However, differences in the spatiospectral and temporal resolution of remote sensing data as well as sensor-specific surveying characteristics very often hinder comparative analyses and effective up- and downscaling analyses. This paper presents a new methodical framework for combining hyperspectral remote sensing data on different spatial and temporal scales. We demonstrate the potential of using the “One Sensor at Different Scales” (OSADIS) approach for the laboratory (plot), field (local), and landscape (regional) scales. By implementing the OSADIS approach, we are able (1) to develop suitable stress-controlled vegetation indices for selected variables such as the Leaf Area Index (LAI), chlorophyll, photosynthesis, water content, nutrient content, etc. over a whole vegetation period. Focused laboratory monitoring can help to document additive and counteractive factors and processes of the vegetation and to correctly interpret their spectral response; (2) to transfer the models obtained to the landscape level; (3) to record imaging hyperspectral information on different spatial scales, achieving a true comparison of the structure and process results; (4) to minimize existing errors from geometrical, spectral, and temporal effects due to sensor- and time-specific differences; and (5) to carry out a realistic top- and downscaling by determining scale-dependent correction factors and transfer functions. The first results of OSADIS experiments are provided by controlled whole vegetation experiments on barley under water stress on the plot scale to model LAI using the vegetation indices Normalized Difference Vegetation Index (NDVI) and green NDVI (GNDVI). The regression model ascertained from imaging hyperspectral AISA-EAGLE/HAWK (DUAL) data was used to model LAI. This was done by using the vegetation index GNDVI with an R ² of 0.83, which was transferred to airborne hyperspectral data on the local and regional scales. For this purpose, hyperspectral imagery was collected at three altitudes over a land cover gradient of 25 km within a timeframe of a few minutes, yielding a spatial resolution from 1 to 3 m. For all recorded spatial scales, both the LAI and the NDVI were determined. The spatial properties of LAI and NDVI of all recorded hyperspectral images were compared using semivariance metrics derived from the variogram. The first results show spatial differences in the heterogeneity of LAI and NDVI from 1 to 3 m with the recorded hyperspectral data. That means that differently recorded data on different scales might not sufficiently maintain the spatial properties of high spatial resolution hyperspectral images.     

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.     

Development of a new automated clean-up system for the simultaneous analysis of polychlorinated dibenzo-p-dioxins (PCDDs), dibenzofurans (PCDFs) and 'dioxin-like' polychlorinated biphenyls (dl-PCB) in flue gas emissions by GPC-SPE

A comprehensive clean-up method for quantitative analysis of polychlorinated biphenyls (PCBs) and polychlorinated dibenzo-p-dioxins and dibenzo-furans (PCDD/Fs) in one single extract of environmental samples was developed. Since the chemical nature and toxicity of planar PCBs are similar to those of PCDD/Fs, dioxin-like PCBs and PCDD/Fs are often surveyed together in their exposure assessments. The development of a method for the simultaneous analysis of PCBs and PCDD/Fs in environmental samples is invaluable. The automated clean-up system evaluated in this work consists of three additional steps after traditional extraction: the chromatography on gel permeation (GPC), the concentration of the solvent through the use of an in-line evaporation module and the further purification and separation of PCDDs/Fs and dl-PCBs on an alumina cartridge in the 'SPE module'. In this work, three fly ash samples from an interlaboratory study with different PCDD/F and PCB levels were Soxhlet-extracted and then cleaned up using an automated system. PCDD/Fs and PCBs were determined using isotope dilution and high resolution gas chromatography/high resolution mass spectrometry. The determined values of 17 PCDD/Fs were consistent with the certified values and the relative standard deviations (RSDs) of the determined values were less than 20%. The recoveries of (13)C labeled PCDD/Fs and planar PCBs, and their RSDs were within the ranges specified in EPA1613 and 1668a methods, respectively. An accurate and reliable method was successfully developed and can be used in the simultaneous analysis of PCDD/Fs and planar PCBs in environmental samples.     

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.     

Assessing the Relative Importance of Spatial Variability in Emissions Versus Landscape Properties in Fate Models for Environmental Exposure Assessment of Chemicals

Multimedia mass balance models differ in their treatment of spatial resolution from single boxes representing an entire region to multiple interconnected boxes with varying landscape properties and emission intensities. Here, model experiments were conducted to determine the relative importance of these two main factors that cause spatial variation in environmental chemical concentrations: spatial patterns in emission intensities and spatial differences in environmental conditions. In the model, experiments emissions were always to the air compartment. It was concluded that variation in emissions is in most cases the dominant source of variation in environmental concentrations. It was found, however, that variability in environmental conditions can strongly influence predicted concentrations in some cases, if the receptor compartments of interest are soil or water—for water concentrations particularly if a chemical has a high octanol–air partition coefficient (K _oa). This information will help to determine the required level of spatial detail that suffices for a specific regulatory purpose.     

Multi-scale Atmospheric Dispersion Modelling by Use of Adaptive Gridding Techniques

An accurate prediction of the transport-reaction behaviour of atmospheric chemical species is required to fully understand the impact on the environment of pollution emissions. Elevated levels of secondary pollutants such as ozone in the lower atmosphere can be harmful to the health of both plants and animals, and can cause damage to property present in the urban environment. Detailed models of pollution mechanisms must therefore be developed through comparisons with field measurements to aid the selection of effective abatement policies. Such models must satisfy accuracy requirements both in terms of the number of species represented, and the spatial resolution of species profiles. Computational expense often compels current models to sacrifice detail in one of these areas. This paper attempts to address the latter point by presenting an atmospheric transport-reaction modelling strategy based upon a finite volume discretisation of the atmospheric dispersion equation. The source terms within this equation are provided by an appropriate reduced chemical scheme modelling the major species in the boundary layer. Reaction and transport discretisations are solved efficiently via a splitting technique applied at the level of the non-linear equations. The solution grid is generated using time dependant adaptive techniques, which provide a finer grid around regions of high spatial error in order to adequately resolve species concentration profiles. The techniques discussed are applied in two dimensions employing emissions from both point and area sources. Preliminary results show that the application of adaptive gridding techniques to atmospheric dynamics modelling can provide more accurately resolved species concentration profiles, accompanied by a reduced CPU time invested in solution. Such a model will provide the basis for high resolution studies of the multiple scale interactions between spatially inhomogeneous source patterns in urban and regional environments.     

Predicting saltwater intrusion into aquifers in vicinity of deserts using spatio-temporal kriging

The primary objective of this study was to provide a detailed framework to use the spatio-temporal kriging to model the spatio-temporal variations of salinity data and predict saltwater intrusion into freshwater aquifers in the vicinity of deserts. EC data, measured in extraction wells in the Mahvelat plain located in the Northeastern part of Iran, available from 2007 to 2013, were used to demonstrate the developed framework. The source of data was not a well-designed measurement network. Therefore, to homogenize the data, spatial analysis was used to find EC distribution in the area in each year of study. To conduct the spatial analysis, a guideline and a systematic process were developed to select an appropriate kriging method and optimize its parameters. This process can be applied to different variables. After spatial analysis of EC data for all the years of the analysis period using empirical Bayesian kriging (EBK) method with manually optimized parameters, spatio-temporal and corresponding variogram analysis was conducted using R software. This process was based on a separable product-sum model applied to the data from 2007 to 2012. The data of 2013 and the data available for the years 1999 and 2006 were used for evaluating the performance of the spatio-temporal model. The EC distribution maps, developed for different years until 2021, show a high level of EC in the north, south, and west of the study area and growing saltwater intrusion into the central freshwater aquifer. This result can be attributed to the over-exploitation of the aquifer and hydraulic head and gradient distribution in the area. The framework provided in this study for spatio-temporal analysis of unstructured EC data is useful for groundwater managers in making proper decisions.     

Integrating classification and regression tree (CART) with GIS for assessment of heavy metals pollution

The classification and regression tree (CART) model integrated with geographical information systems and the assessment of heavy-metals pollution system was developed to assess the heavy metals pollution in Fuyang, Zhejiang, China. The integration of the decision tree model with ArcGIS Engine 9 using a COM implementation in Microsoft® Visual Basic 6.0 provided an approach for assessing the spatial distribution of soil Zn content with high predictive accuracy. The Zn concentration classes estimated by CART assigned the right classes with an accuracy of near 90%. This is a great improvement compared to the ordinary Kriging method for the spatial autocorrelation of the study area severely destroyed by human activities. Also, it can be used to investigate the inter-relationships between the heavy metals pollution and environmental and anthropogenic variables. Moreover, the research presents model predictions over space for further applications and investigations.     

Enhanced Wet Deposition Estimates using Modeled Precipitation Inputs

Effective assessments of linkages between atmospheric deposition and sensitive aquatic and terrestrial ecosystems require a knowledge of spatial patterns in deposition of greater resolution than currently available using point estimates and two-dimensional surface algorithms. A high resolution wet deposition model was developed for the Eastern U.S.A. by incorporating daily precipitation measurements from National Oceanic and Atmospheric Administration (NOAA) monitoring sites located within the Eastern U.S.A.; topographic variables, including elevation, slope, and aspect that affect the amount and distribution of precipitation across the region; and precipitation chemistry data from National Atmospheric Deposition Program (NADP) sites within and adjacent to the region. Model performance, which was tested against independent measurements from three physiographic regions of the eastern U.S.A., revealed a statistically significant reduction in interpolation error compared to traditional two-dimensional surface algorithms.     

Accurate Ozone Prognostic Patterns for Madrid Area by Using a High Spatial and Temporal Eulerian Photochemical Model

The ANA Air Quality Model (ANA stands for Atmospheric Mesoscale Numerical Pollution Model for Regional and Urban Areas) has been applied over Madrid during a five day period in June, 1995. The domain is 80 × 100 km2 and the spatial resolution is 2000 m. The ANA system is driven by a meteorological model REMEST and it includes a detailed emission model for anthropogenic and biogenic sources with 250 m spatial resolution and 60 minutes temporal resolution. Different deposition processes are used such as the Wesely (1989) and Erisman et al. (1994) resistance approaches and the simple aerodynamic resistance. The photochemical processes and the general chemistry is based on the CBM-IV mechanism for the organic compounds and solved by the SMVGEAR method (CHEMA module).The model uses 14 different landuse types which are obtained by using the REMO module which uses the information provided by the LANDSAT-5 satellite image over the domain. The emission module EMIMA takes into account the point, line and area emissions over the domain. Special importance is given to the biogenic emissions which are obtained by using the satellite landuse classification for caducous, perenneal and mixed terrain. The emission module considers the EPA and CORINAIR emission factors. The results show an accurate prediction of the ozone maxima for the five days and also the general pattern of the ozone observed data. The five day simulation is characterized by a local low pressure over the Madrid Area and high pressures over Spain and West of Europe. The ozone surface patterns show the diurnal cycle and the maxima concentrations up to 140-160 ppb for suburban areas during afternoon hours. The general performance of the model is considered quite good. The computer power requirements continue to be very high for standard workstations. Future progress on parallel platforms should improve considerably the computer time requirements.     

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。     

Monitoring the changing position of coastlines using aerial and satellite image data: an example from the eastern coast of Trabzon, Turkey

Coastline mapping and coastline change detection are critical issues for safe navigation, coastal resource management, coastal environmental protection, and sustainable coastal development and planning. Changes in the shape of coastline may fundamentally affect the environment of the coastal zone. This may be caused by natural processes and/or human activities. Over the past 30 years, the coastal sites in Turkey have been under an intensive restraint associated with a population press due to the internal and external touristic demand. In addition, urbanization on the filled up areas, settlements, and the highways constructed to overcome the traffic problems and the other applications in the coastal region clearly confirm an intensive restraint. Aerial photos with medium spatial resolution and high resolution satellite imagery are ideal data sources for mapping coastal land use and monitoring their changes for a large area. This study introduces an efficient method to monitor coastline and coastal land use changes using time series aerial photos (1973 and 2002) and satellite imagery (2005) covering the same geographical area. Results show the effectiveness of the use of digital photogrammetry and remote sensing data on monitoring large area of coastal land use status. This study also showed that over 161 ha areas were filled up in the research area and along the coastal land 12.2 ha of coastal erosion is determined for the period of 1973 to 2005. Consequently, monitoring of coastal land use is thus necessary for coastal area planning in order to protecting the coastal areas from climate changes and other coastal processes.     

A GIS-based approach for the long-term prediction of human health risks at contaminated sites

A Health Index/Risk Evaluation Tool (HIRET) has been developed for the integration of risk assessment and spatial planning using GIS capabilities. The method is meant to assist decision makers and site owners in the evaluation of potential human health risk with respect to land use. Human health risk defined as the potential adverse effects on human life or health is generally accepted as the most important aspect for site assessment and planning of remediation strategies. It concerns polluted sites that endanger human health on one hand and derelict land that does not cause the immediate risk on the other hand. In current state-of-the-art risk-assessment, long-term spatial and temporal changes of risks, in relation to changes in contamination patterns and land use functions, are not taken into account. The aim of this paper is to demonstrate the methodology developed for human health risk assessment in aspect of spatial and temporal domain. HIRET was developed as an extension for ESRI software ArcView 3.2 and allows performing dynamic human health risk assessment in long-term period, which is relevant for land use planning. The paper illustrates how such methodology can assist in environmental decision-making to enhance the efficiency of contaminated land management. A case study of contaminated site is given showing how data can be used within a GIS framework to produce maps indicating areas of potential human health risk.     

Monitoring and assessing of landscape heterogeneity at different scales

In numerous studies, spatial and spectral aggregations of pixel information using average values from imaging spectrometer data are suggested to derive spectral indices and the subsequent vegetation parameters that are derived from these. Currently, there are very few empirical studies that use hyperspectral data, to support the hypothesis for deriving land surface variables from different spectral and spatial scales. In the study at hand, for the first time ever, investigations were carried out on fundamental scaling issues using specific experimental test flights with a hyperspectral sensor to investigate how vegetation patterns change as an effect of (1) different spatial resolutions, (2) different spectral resolutions, (3) different spatial and spectral resolutions as well as (4) different spatial and spectral resolutions of originally recorded hyperspectral image data compared to spatial and spectral up- and downscaled image data. For these experiments, the hyperspectral sensor AISA-EAGLE/HAWK (DUAL) was mounted on an aircraft to collect spectral signatures over a very short time sequence of a particular day. In the first experiment, reflectance measurements were collected at three different spatial resolutions ranging from 1 to 3 m over a 2-h period in 1 day. In the second experiment, different spectral image data and different additional spatial data were collected over a 1-h period on a particular day from the same test area. The differently recorded hyperspectral data were then spatially and spectrally rescaled to synthesize different up- and down-rescaled images. The normalised difference vegetation index (NDVI) was determined from all image data. The NDVI heterogeneity of all images was compared based on methods of variography. The results showed that (a) the spatial NDVI patterns of up- and downscaled data do not correspond with the un-scaled image data, (b) only small differences were found between NDVI patterns determined from data recorded and resampled at different spectral resolutions and (c) the overall conclusion from the tests carried out is that the spatial resolution is more important in determining heterogeneity by means of NDVI than the depth of the spectral data. The implications behind these findings are that we need to exercise caution when interpreting and combining spatial structures and spectral indices derived from satellite images with differently recorded geometric resolutions.     

Influence of spatial resolution on assessing channelization impacts on fish and macroinvertebrate communities in a warmwater stream in the southeastern United States

The choice of spatial and temporal scale used in environmental assessments may influence the observed results. One method of assessing the impact of stream habitat alterations involves the comparison of response variables among treatment categories (i.e., impacted and unimpacted sites). The influence of spatial resolution on patterns of response variables among treatment categories in assessments of stream channelization and other types of habitat alterations has not been evaluated. We examined how patterns of 10 community response variables among channel types and our interpretations of channelization impacts on fish and macroinvertebrate communities differed among three spatial resolutions in a warmwater stream in Mississippi and Alabama. Four fish and three macroinvertebrate community response variables exhibited different patterns among channel types at different spatial resolutions. Our interpretations of the impacts of channelization on fish and macroinvertebrate communities differed among spatial resolutions. Channelization had a negative influence on fish communities either with or without evidence of potential community recovery in one channel type. Channelization impacts on macroinvertebrate communities ranged from a negative influence to no effect. Our results suggest that spatial resolution can influence the observed results and interpretations derived from assessments of stream habitat alterations.     

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.