Land Use Database Research Articles

Impact of Land Use and Land Cover Change on Deforestation in the Central Taraba State: A Geographic Information System and Remote Sensing Analysis

Deforestation, the widespread clearance of forests for various land uses, has become a significant global environmental issue with far-reaching consequences. Deforestation in the study area was identified, categorized, assessed, and interpreted using Landsat 5, 7, and 8 from the years 2008, 2014, and 2020, respectively. A geographic information system (GIS) database of land use and land cover categories and their changes were created. The results showed that several anthropogenic human activities, including agriculture and wood harvesting, were driving a general retreat of the forest area. The study further shows that between 2008 and 2020, forest area decreased by 8.5% with an annual rate of 0.33%, other vegetative areas increased by 2% with an annual rate of 0.077%, and non-vegetative areas increased by 1.5% with an annual rate of 0.58%. The hotspot map shows that the rate at which the reserve is deteriorating and the conversion of the forest area to other vegetation and bare ground are only a matter of time. The study recommended that the government should adopt rigorous policies to protect forest reserves from unauthorized habitation by encouraging the use of alternative firewood fuel sources to reduce the pressure on the forest.

Possible Projection of the First Military Survey of the Habsburg Empire in Lower Austria and Hungary (Late 18th Century)—An Improvement in Fitting Historical Topographic Maps to Modern Cartographic Systems

Map mosaics of the First Military Survey showing Lower Austria and Hungary (two separate zones and coordinate systems of 1:28,800 survey sections) were georeferenced. Compared to the previous fitting carried out in the framework of the publicly available MAPIRE project, an attempt was made here to determine the true native projections, despite the assumption, according to the literature, that these map works have no real geodetic basis and no real cartographic projection. In the case of Lower Austria, the native coordinate system of the Brno–Wien–Varaždin degree measurement of Father Joseph Liesganig, the Cassini projection centred on Stephansdom in Vienna, proved to be the survey’s own projection. In Hungary, in addition to the centre of a similar degree measurement, a fundamental point of the also documented Budapest-surrounding network of Colonel Neu proved to be a possible starting point of the Cassini projection used. Thus, with these centres, the Cassini projection is a good mathematical model for the native coordinate system of the surveys in these provinces. This achievement opens the possibility of better georeferencing of old maps of the survey, providing a database of land use and environmental change analyses and a step forward in understanding the survey technology of the 18th century.

Improving the database on land use planning in Vung Tau city, Ba Ria - Vung Tau province

Vung Tau city is an area where management of land use planning (LUP) and urban planning is quite complicated and has been successful in building LUP database. However, this database still has limitations that must be addressed, including (1) lack of data layer on current land use for building LUP database, (2) incompleted LUP attribute database, (3) lack of metadata, (4) no connection between LUP database and digital legal materials and (5) no overlay cadastral database to data layer of current land use and LUP database. To solve these problems, this research used many methods such as: documents and data collection, inheritance, expert interview, data processing and analysis, mapping, GIS application, and IT applications. The results of this study showed that the structure of cadastral database of 8 wards was succesfully converted from existing structure (according to Circular 17/2010/TT-BTNMT) to the standard structure (according to Circular 75/2015/TT-BTNMT). The 2019 land inventory data was standardized and data layer of current land use for whole city was built. In addition, the LUP database (period 2010 - 2020) for Vung Tau was improved, including: supplementing attribute information, completing metadata, connecting LUP database with relevant digital legal records, overlaying cadastral database with LUP database and data layer of current land use. Briefly, our results could be a foundation to help Vung Tau manage land sustainably, contribute to successfully building national land database and meet the exploitation requirements of LUP information in accordance with the orientation of Ministry of Natural Resources and Environment for study area.

Modeling Topsoil Phosphorus—From Observation-Based Statistical Approach to Land-Use and Soil-Based High-Resolution Mapping

Phosphorus (P) is a macronutrient that often limits the productivity and growth of terrestrial ecosystems, but it is also one of the main causes of eutrophication in aquatic systems at both local and global levels. P content in soils can vary largely, but usually, only a small fraction is plant-available or in an organic form for biological utilization because it is bound in incompletely weathered mineral particles or adsorbed on mineral surfaces. Furthermore, in agricultural ecosystems, plant-available P content in topsoil is mainly controlled by fertilization and land management. To understand, model, and predict P dynamics at the landscape level, the availability of detailed observation-based P data is extremely valuable. We used more than 388,000 topsoil plant-available P samples from the period 2005 to 2021 to study spatial and temporal variability and land-use effect on soil P. We developed a mapping approach based on existing databases of soil, land-use, and fragmentary soil P measurements by land-use classes to provide spatially explicit high-resolution estimates of topsoil P at the national level. The modeled spatially detailed (1:10,000 scale) GIS dataset of topsoil P is useful for precision farming to optimize nutrient application and to increase productivity; it can also be used as input for biogeochemical models and to assess P load in inland waters and sea.

Effects of different spatial and precipitation input data on swat-derived catchment features

Computer-based mathematical models are used in water management research to represent ecological problems, simulate different processes, deal with such problems and support their solution. Such studies require an interdisciplinary approach that is both user-friendly and comprehensive to integrate all of the processes that occur in nature. The use of available hydrological models requires a model setup with a reasonable level of data quality and quantity to represent catchment features to emphasize the distinctive local character of ecosystems. One of the models that comply with such requirements is the Soil and Water Assessment Tool (SWAT). This paper aims to address the possible impact of different spatial and precipitation input data on the catchment features by using SWAT for the case study of the floodplain located within the Danube River Basin in Serbia. The objective was to evaluate the impact of digital elevation models, land use, and soil types with different resolutions (1) SRTM and ASTER (30 m), TanDEM-X (12.5 m), (2) CORINE and GlobCover land use databases, and (3) FAO/UNESCO world soil map and digitized soil map of Vojvodina Province on catchment delineation. The research was conducted alongside the analysis of precipitation, using data from the CFSR, CarpatClim, and the national yearbooks. Regarding the spatial data, the results indicate that the high-resolution data need to be adjusted for this area, while the ASTER layer is suitable at an acceptable level for further modeling in SWAT. Interpolated precipitation data are better to use due to their higher resolution (10km) and the heterogeneous distribution of rain gauge stations.

Aplikasi Geographic Information System dan Remote Sensing untuk Pengembangan Potensi Wisata Agro di Kawasan Objek Wisata Air Terjun Tegenungan, Desa Kemenuh

Wiradhika Mahayasa Putra. NIM. 1706541045. GIS Application and Remote Sensing for Agrotourism Potential Development In Tegenungan Waterfall Tourism Area, Kemenuh Village, Sukawati District. The implementation of agricultural and tourism activities is an agro-tourism concept can be synergized with other natural tourism objects such as waterfalls, Tegenungan Waterfall tourist attraction areas, Kemenuh Village, agro-tourism potential development can be carried out. The purpose of this research is to inform the area database based on GIS and remote sensing, identification and information on agro-tourism potential, mapping of tourist attractions and planning of locations for agro-tourism objects. Research methods: literature study, interpretation of satellite imagery, recording of information on potential tourist objects, calculation of the status of potential tourist objects, recording of regional databases, identification of agro-tourism objects, calculation and division of potential classes, mapping of tourist attractions. The results of this study indicate the value of the potential class of Tegenungan Waterfall tourism objects is in the good category with a value of 28. The map of the area displays a database of area, road length and land use, the value of the agro-tourism destination plan are classified as good with a value of 25, tourist information maps present an overview of tourist attractions at the location, the agro-tourism plan map displays the agro- tourism object plan: rice field cycling, plowing, canang making, educational tours, banana and coconut biodiversity and subak view.

An Advanced Open Land Use Database as a Resource to Address Destination Earth Challenges

Land-use and land-cover (LULC) themes are important for many domains, especially when they process environmental and socio-economic phenomena. The evolution of a land-use database called Open Land Use (OLU) started in 2013 and was continued by adapting many user requirements. The goal of this study was to design a new version of the OLU database that would better fit the gathered user requirements collected by projects using LULC data. A formal definition of the developed data model through Unified Modeling Language (UML) class diagrams, a feature catalogue based on ISO 19110 and SQL scripts for setting up the OLU database, are the key achievements of the presented paper. The presented research provides a multi-scale open database of LULC information supporting the DestinE initiative to develop a very-high-precision digital model of the earth. The novel spatio-temporal thematic approach also lies in modular views of the OLU database.

Spatial evolution of cultivated land in the Heilongjiang Province in China from 1980 to 2015.

Sustaining cultivated land is critically important for food security, economic development, and social stability in China. China has been developing rapidly since the adoption of the Chinese Economic Reform of 1978, revealing the spatial evolution characteristics of cultivated land can provide valuable information for estimating and sustaining Chinese food security. In this study, we analyzed the spatial evolution of cultivated land from 1980 to 2015 in the Heilongjiang Province, one of the most important grain production regions in China, by combining five land-use databases, a geographic information system, and mathematical models. The results showed that cultivated land in the Heilongjiang Province increased by 33,234 km2 (23%) from 1980 to 2015. The increase primarily resulted from land conversion from the forest (11,511 km2), grassland (9780 km2), and unused land (9696 km2). Additionally, cultivated land-use change's spatial characteristics differed among decadal periods. Spatial changes were stronger during 1990-2000 than in the other periods (1980-1990, 2000-2010, and 2010-2015). The landscape shape index indicated that cultivated land development had a regular changing pattern but showed a trend toward irregular development over the study period. The spatial expansion of cultivated land showed a strong directional trend, which was further found to be associated with geomorphological factors, indicating the prominent controls of abiotic factors on cultivated land expansion. This study provides an overview of the long-term spatial evolution characteristics of cultivated land and the key abiotic controls on cultivated land use change. This information can help local government policymakers effectively balance regional development, environmental protection, and food security.

Modelling geographical accessibility to urban centres in Kenya in 2019.

BackgroundAccess to major services, often located in urban centres, is key to the realisation of numerous Sustainable Development Goals (SDGs). In Kenya, there are no up-to-date and localised estimates of spatial access to urban centres. We estimate the travel time to urban centres and identify marginalised populations for prioritisation and targeting.MethodsUrban centres were mapped from the 2019 Kenya population census and combined with spatial databases of road networks, elevation, land use and travel barriers within a cost-friction algorithm to compute travel time. Seven travel scenarios were considered: i) walking only (least optimistic), ii) bicycle only, iii) motorcycle only, iv) vehicle only (most optimistic), v) walking followed by motorcycle transport, vi) walking followed by vehicle transport, and vii) walking followed by motorcycle and then vehicle transport (most pragmatic). Mean travel time, and proportion of the population within 1-hour and 2-hours of the urban centres were summarized at sub-national units (counties) used for devolved planning. Inequities were explored and correlations between the proportion of the population within 1-hour of an urban centre and ten SDG indicators were computed.ResultsA total of 307 urban centres were digitised. Nationally, the mean travel time was 4.5-hours for the walking-only scenario, 1.0-hours for the vehicle only (most optimistic) scenario and 1.5-hours for the walking-motorcycle-vehicle (most pragmatic) scenario. Forty-five per cent (21.3 million people) and 87% (41.6 million people) of Kenya’s population resided within 1-hour of the nearest urban centre for the least optimistic and most pragmatic scenarios respectively. Over 3.2 million people were considered marginalised or living outside the 2-hour threshold in the pragmatic scenario, 16.0 million Kenyans for walking only, and 2.2 million for the most optimistic scenario. County-level spatial access was highly heterogeneous ranging between 8%-100% and 32%-100% of people within the 1-hour threshold for the least and most optimistic scenarios, respectively. Counties in northern and eastern parts of Kenya were generally most marginalised. The correlation coefficients for nine SDG indicators ranged between 0.45 to 0.78 and were statistically significant.ConclusionTravel time to urban centres in Kenya is heterogeneous. Therefore, marginalised populations should be prioritised during resource allocation and policies should be formulated to enhance equitable access to public services and opportunities in urban areas.

Numerical study of turbulent flow fields in urban areas using modified [formula omitted] model and large eddy simulation

In this study, a numerical urban model is proposed, which includes digital elevation model for topography, land-use database, digital maps on buildings and vegetation. Turbulent flow fields in the cube arrays with different packing densities and a real urban area are investigated by both modified k−ε model and large eddy simulation (LES). It is found that the simulation with the modified k−ε model is less sensitive with grid resolution and is able to predict turbulent flow fields over uniform urban-like cube arrays, where the turbulent flow fields are fully developed. However, the modified k−ε model shows some discrepancies between the predicted and measured turbulent flow fields in the urban area, where the flow fields are strongly affected by the organized motions generated by surrounding buildings. The LES model is able to predict turbulent flow fields in the uniform urban-like cube arrays as well as in the non-uniform urban area. The accuracy of LES model strongly affected by the grid resolution and a grid dependency study is conducted in the target area.

Highly Resolved Rainfall-Runoff Simulation of Retrofitted Green Stormwater Infrastructure at the Micro-Watershed Scale

Green Stormwater Infrastructure (GSI), a sustainable engineering design approach for managing urban stormwater runoff, has long been recommended as an alternative to conventional conveyance-based stormwater management strategies to mitigate the adverse impact of sprawling urbanization. Hydrological and hydraulic simulations of small-scale GSI measures in densely urbanized micro watersheds require high-resolution spatial databases of urban land use, stormwater structures, and topography. This study presents a highly resolved Storm Water Management Model developed under considerable spatial data constraints. It evaluates the cumulative effect of the implementation of dispersed, retrofitted, small-scale GSI measures in a heavily urbanized micro watershed of Costa Rica. Our methodology includes a high-resolution digital elevation model based on Google Earth information, the accuracy of which was sufficient to determine flow patterns and slopes, as well as to approximate the underground stormwater structures. The model produced satisfactory results in event-based calibration and validation, which ensured the reliability of the data collection procedure. Simulating the implementation of GSI shows that dispersed, retrofitted, small-scale measures could significantly reduce impermeable surface runoff (peak runoff reduction up to 40%) during frequent, less intense storm events and delay peak surface runoff by 5–10 min. The presented approach can benefit stormwater practitioners and modelers conducting small scale hydrological simulation under spatial data constraint.

MAKING SMART URBAN DECISIONS: THE NICHE OF A PARAMETRIC SPATIAL MODEL TO BALANCE THE NEEDS OF URBAN STORMWATER MANAGEMENT AND HUMAN WELLBEING

Abstract. Rapid urbanization has resulted in high-density construction, more impervious area and with increasing threats of urban stormwater, drought and negative influences on human wellbeing. Although there has been progress in sustainable stormwater management and the promotion of human wellbeing with green space, the research has been conducted from a mono-disciplinary perspective and has brought potential conflicts in the utilisation of green space in an urban area. Since smart city proposed, researchers are exploring informational and integrated urban management to face this type of development conflicts and achieve a harmonious and sustainable future. This research proposes a parametric spatial model to integrate stormwater management and human recreation needs based on the understanding of the relationship between them on the same urban green space. We expect that this integrated parametric spatial model will help residents to interact with functional green space and provide options to organize the urban green space smarter and more effectively. This paper presents: (1) A review of the main sustainable stormwater management measures with principles, models and facilities to reveal an overlooked but important relationship between space and stormwater management. (2) Investigation of the research on the benefits of green space on human wellbeing. The study argues that the participation of green space cannot be effectively and accurately analysed with the current main analysis measures which relying on satellite-based vegetation indices or land-use database. This research is intended to set smarter decision making on urban green spaces which connects sustainable stormwater management with human wellbeing via visualised parametric spatial model.

Global soil, landuse, evapotranspiration, historical and future weather databases for SWAT Applications

Large-scale distributed watershed models are data-intensive, and preparing them consumes most of the research resources. We prepared high-resolution global databases of soil, landuse, actual evapotranspiration (AET), and historical and future weather databases that could serve as standard inputs in Soil and Water Assessment Tool (SWAT) models. The data include two global soil maps and their associated databases calculated with a large number of pedotransfer functions, two landuse maps and their correspondence with SWAT’s database, historical and future daily temperature and precipitation data from five IPCC models with four scenarios; and finally, global monthly AET data. Weather data are 0.5° global grids text-formatted for direct use in SWAT models. The AET data is formatted for use in SWAT-CUP (SWAT Calibration Uncertainty Procedures) for calibration of SWAT models. The use of these global databases for SWAT models can speed up the model building by 75–80% and are extremely valuable in areas with limited or no physical data. Furthermore, they can facilitate the comparison of model results in different parts of the world.

Residential surrounding green, air pollution, traffic noise and self-perceived general health

Self-perceived general health (SGH) is one of the most inclusive and widely used measures of health status and a powerful predictor of mortality. However, only a limited number of studies evaluated associations of combined environmental exposures on SGH. Our aim was to evaluate associations of combined residential exposure to surrounding green, air pollution and traffic noise with poor SGH in the Netherlands. We linked data on long-term residential exposure to surrounding green based on the Normalized Difference Vegetation Index (NDVI) and a land-use database (TOP10NL), air pollutant concentrations (including particulate matter (PM10, PM2.5), and nitrogen dioxide (NO2)) and road- and rail-traffic noise with a Dutch national health survey, resulting in a study population of 354,827 adults. We analyzed associations of single and combined exposures with poor SGH. In single-exposure models, NDVI within 300 m was inversely associated with poor SGH [odds ratio (OR) = 0.91, 95% CI: 0.89, 0.94 per IQR increase], while NO2 was positively associated with poor SGH (OR = 1.07, 95% CI: 1.04, 1.11 per IQR increase). In multi-exposure models, associations with surrounding green and air pollution generally remained, but attenuated. Joint odds ratios (JOR) of combined exposure to air pollution, rail-traffic noise and decreased surrounding green were higher than the odds ratios of single-exposure models. Studies including only one of these correlated exposures may overestimate the risk of poor SGH attributed to the studied exposure, while underestimating the risk of combined exposures.

Monitoring Land Use Change for Gentrification in Soho (London), Using Geographic Information Systems and Google Street View

Gentrification involves the substantial replacement of a neighborhood's lower-income residents with newcomers of higher income who renovate and upgrade the neighborhood. Although gentrification is first and foremost a residential process, it also entails commercial redevelopment. As the socioeconomic composition of the neighborhood changes, so it does the perception of acceptable land uses. In Soho, displacement has affect residents and local business in the same way, but the attention has been focused in the cultural and entertainment activities that have been currently disappearing, since once they were the heart of this neighborhood. The principal aim of this study is to develop a methodology to keep geographic land-use databases updated in order to analyze the spatial variability related with gentrification process of Soho. This study shows a methodology to keep geographic land-use database updated using Geographic Information Systems, through the use of panoramic photos captured by Google Street View. Since the panoramic photos from different years are now available, Google Street View could become a new tool to perceive, observe and study neighborhood changes in Soho during the years 2008, 2012, 2014 and 2015. Based on the obtained maps, there is a representative spatial variability that land uses show in Soho, as regardless the reduced extension of this area, the centroids are constantly moving from year to year. Therefore, Geographic Information Systems and Google Street View are useful tools to keep updated geodatabases; however, for future work it is recommended the inclusion of Volunteered Geographic Information.

Associations of combined exposures to surrounding green, air pollution and traffic noise on mental health

BackgroundEvidence is emerging that poor mental health is associated with the environmental exposures of surrounding green, air pollution and traffic noise. Most studies have evaluated only associations of single exposures with poor mental health. ObjectivesTo evaluate associations of combined exposure to surrounding green, air pollution and traffic noise with poor mental health. MethodsIn this cross-sectional study, we linked data from a Dutch national health survey among 387,195 adults including questions about psychological distress, based on the Kessler 10 scale, to an external database on registered prescriptions of anxiolytics, hypnotics & sedatives and antidepressants. We added data on residential surrounding green in a 300 m and a 1000 m buffer based on the Normalized Difference Vegetation Index (NDVI) and a land-use database (TOP10NL), modeled annual average air pollutant concentrations (including particulate matter (PM10, PM2.5), and nitrogen dioxide (NO2)) and modeled road- and rail-traffic noise (Lden and Lnight) to the survey. We used logistic regression to analyze associations of surrounding green, air pollution and traffic noise exposure with poor mental health. ResultsIn single exposure models, surrounding green was inversely associated with poor mental health. Air pollution was positively associated with poor mental health. Road-traffic noise was only positively associated with prescription of anxiolytics, while rail-traffic noise was only positively associated with psychological distress. For prescription of anxiolytics, we found an odds ratio [OR] of 0.88 (95% CI: 0.85, 0.92) per interquartile range [IQR] increase in NDVI within 300 m, an OR of 1.14 (95% CI: 1.10, 1.19) per IQR increase in NO2 and an OR of 1.07 (95% CI: 1.03, 1.11) per IQR increase in road-traffic noise. In multi exposure analyses, associations with surrounding green and air pollution generally remained but attenuated. Joint odds ratios [JOR], based on the Cumulative Risk Index (CRI) method, of combined exposure to air pollution, traffic noise and decreased surrounding green were higher than the ORs of single exposure models. Associations of environmental exposures with poor mental health differed somewhat by age. ConclusionsStudies including only one of these three correlated exposures may overestimate the influence of poor mental health attributed to the studied exposure, while underestimating the influence of combined environmental exposures.

EVALUATION OF THE LONG-TERM EFFECTS OF EXPOSURE TO GREENSPACE ON TYPE 2 DIABETIC PATIENTS: CASE STUDY – TEHRAN, IRAN

Abstract. Development of information technology and expansion of geospatial information systems have realized the planning managers and urban policy-makers’ wishes in making more informed decisions about urban management. At the same time, population growth and the provision of its health should be considered as one of the most important and remarkable issues for many researchers and medical specialists. So, in recent years there have been an increasing number of researches related to the study of effective factors such as environment parameters on the people’s health. In previous research, the long-term exposure effects of environmental parameters such as greenspace and air pollution on people’s health have been mostly ignored or access to reliable data has not been accomplished. The aim of this research is to study how the long-term exposure to greenspace surrounding the type 2 diabetes mellitus (T2DM) affects the average values of four years glycolized hemoglobin (HbA1c) levels. Moreover, in order to study the effects of the data type on reliability of the results, land-use data base (LDB) and satellite imagery have been employed. Pearson product and regression model have been used in this research for correlation and buffer analyse to calculate the degree of exposure of T2DM persons to greenspace. According to the results, negative correlation between long-term exposure to greenspace and the average values of four years HbA1c levels becomes statistically significant. Pearson correlation coefficients for the LDB (r = −0.366, p = 0.001) and satellite imagery (r = −0.276, p = 0.006) at 250-meter buffer from diabetic patients’ habitat is significant at 99% confidence level.

Land-use changes in Slovenia from the Franciscean Cadaster until today

The Franciscean Cadaster from the first half of the nineteenth century is an excellent source for studying land use and its changes. However, to date it has only rarely been used in geographical and historical research at the regional or national level. Setting up a digital database of land use recorded in the Franciscean Cadaster at the level of cadastral municipalities covering all of Slovenia and incorporating it into a geographic information system has provided an opportunity for detailed studies of land-use changes spanning two centuries. This article presents the first analyses of changes in individual land-use types and the typology of changes across two centuries.

Effect of land use change on ecosystem services in Lake Balaton Catchment

Land Use and Land Cover Changes (LULCC) are interrelated with other global processes like global climate change, population growth, land degradation etc. Land use structure of a given area, region is adapted to the requirements of human society since the appearance of human beings. Land use structure is understood as the proportion of land use types orruring in a given area. The objective of the paper is to analyse land use changes in Lake Balaton catchment, Hungary and to present the role of changes of one ecosystem service, i.e. recreation and tourism by quantitative methods, based on spatial and statistical databases of land use and tourism in Lake Balaton catchment, Hungary between 1990 and 2012. The study area is a frequented touristic region of Hungary with variegated and beautiful landscape mosaics. The lake (595 km2) is exposed to various kinds of environmental impacts including agricultural activities and tourism. For the quantification of the Recreation and Ecotourism Ecosystem Service potential the Multi-Criteria Evaluation (MCE) method was applied.Land use changes between 1990 and 2012 were identified from the CORINE Land Cover Database and evaluated from ecological aspects and from the aspect of ecosystem services. The main changes include a continuous increase of the forest area and a remarkable on-going decrease of arable land. Soil erosion as the main land degradation process in the area endangering the quality of ecosystem services. The remarkable decrease of arable fields reduces erosion risk. Ecosystem services provided by each land use type were analysed. The main conclusion is that long term trend of land use changes is beneficial for ecosystem services.

Emissions preparation and analysis for multiscale air quality modeling over the Athabasca Oil Sands Region of Alberta, Canada

Abstract. The oil sands (OS) of Alberta, Canada, which are classified as unconventional oil, are the third-largest oil reserves in the world. We describe here a 6-year effort to improve the emissions data used for air quality (AQ) modeling of the roughly 100 km × 100 km oil extraction and processing industrial complex operating in the Athabasca Oil Sands Region (AOSR) of northeastern Alberta. This paper reviews the national, provincial, and sub-provincial emissions inventories that were available during the three phases of the study, supplemented by hourly SO2 and NOx emissions and stack characteristics for larger point sources measured by a continuous emission monitoring system (CEMS), as well as daily reports of SO2 from one AOSR facility for a 1-week period during a 2013 field campaign when the facility experienced upset conditions. Next it describes the creation of several detailed hybrid emissions inventories and the generation of model-ready emissions input files for the Global Environmental Multiscale–Modelling Air quality and CHemistry (GEM-MACH) AQ modeling system that were used during the 2013 field study and for various post-campaign GEM-MACH sensitivity studies, in particular for a high-resolution model domain with 2.5 km grid spacing covering much of western Canada and centered over the AOSR. Lastly, it compares inventory-based bottom-up emissions with aircraft-observation-based top-down emissions estimates. Results show that emissions values obtained from different data sources can differ significantly, such as a possible 10-fold difference in PM2.5 emissions and approximately 40 and 20 % differences for total VOC (volatile organic compound) and SO2 emissions. A novel emissions-processing approach was also employed to allocate emissions spatially within six large AOSR mining facilities in order to address the urban-scale spatial extent of the facilities and the high-resolution 2.5 km model grid. Gridded facility- and process-specific spatial surrogate fields that were generated using spatial information from GIS (geographic information system) shapefiles and satellite images were used to allocate non-smokestack emissions for each facility to multiple grid cells instead of treating these emissions as point sources and allocating them to a single grid cell as is normally done. Facility- and process-specific temporal profiles and VOC speciation profiles were also developed. The pre-2013 vegetation and land-use databases normally used to estimate biogenic emissions and meteorological surface properties were modified to account for the rapid change in land use in the study area due to marked, year-by-year changes in surface mining activities, including the 2013 opening of a new mine. Lastly, mercury emissions data were also processed in addition to the seven criteria-air-contaminant (CAC) species (NOx, VOC, SO2, NH3, CO, PM2.5, and PM10) to support AOSR mercury modeling activities. Six GEM-MACH modeling papers in this special issue used some of these new sets of emissions and land-use input files.