Understanding interactions between urban development policies and GHG emissions: A case study in Stockholm Region

Human-induced urban growth and sprawl have implications for greenhouse gas (GHG) emissions that may not be included in conventional GHG accounting methods. Improved understanding of this issue requires use of interactive, spatial-explicit social–ecological systems modeling. This paper develops a comprehensive approach to modeling GHG emissions from urban developments, considering Stockholm County, Sweden as a case study. GHG projections to 2040 with a social–ecological system model yield overall greater emissions than simple extrapolations in official climate action planning. The most pronounced difference in emissions (39% higher) from energy use single-residence buildings resulting from urban sprawl. And this difference is not accounted for in the simple extrapolations. Scenario results indicate that a zoning policy, restricting urban development in certain areas, can mitigate 72% of the total emission effects of the model-projected urban sprawl. The study outcomes include a decision support interface for communicating results and policy implications with policymakers.

     

Effects of land use data on dry deposition in a regional photochemical model for eastern Texas

Land use data are among the inputs used to determine dry deposition velocities for photochemical grid models such as the Comprehensive Air Quality Model with extensions (CAMx) that is currently used for attainment demonstrations and air quality planning by the state of Texas. The sensitivity of dry deposition and O3 mixing ratios to land use classification was investigated by comparing predictions based on default U.S. Geological Survey (USGS) land use data to predictions based on recently compiled land use data that were collected to improve biogenic emissions estimates. Dry deposition of O3 decreased throughout much of eastern Texas, especially in urban areas, with the new land use data. Predicted 1-hr averaged O3 mixing ratios with the new land use data were as much as 11 ppbv greater and 6 ppbv less than predictions based on USGS land use data during the late afternoon. In addition, the area with peak O3 mixing ratios in excess of 100 ppbv increased significantly in urban areas when deposition velocities were calculated based on the new land use data. Finally, more detailed data on land use within urban areas resulted in peak changes in O3 mixing ratios of approximately 2 ppbv. These results indicate the importance of establishing accurate, internally consistent land use data for photochemical modeling in urban areas in Texas. They also indicate the need for field validation of deposition rates in areas experiencing changing land use patterns, such as during urban reforestation programs or residential and commercial development.     

Application of a scenario-based modeling system to evaluate the air quality impacts of future growth

The structure and design of future urban development can have significant adverse effects on air pollutant emissions as well as other environmental factors. When considering the future impact of growth on mobile source emissions, we generally model the increase in vehicle kilometers traveled (VKT) as a function of population growth. However, diverse and poorly planned urban development (i.e., urban sprawl) can force higher rates of motor vehicle use and in return increase levels of pollutant emissions than alternative land-use scenarios. The objective of this study is to develop and implement an air quality assessment tool that takes into account the influence of alternative growth and development scenarios on air quality.The use of scenario-based techniques in land use planning has been around since the late 1940s and been tested in many different applications to aid in decision-making. In this study, we introduce the development of an advanced interactive scenario-based land use and atmospheric chemistry modeling system coupled with a GIS (Geographical Information System) framework. The modeling system is designed to be modular and includes land use/land cover information, transportation, meteorological, emissions, and photochemical modeling components. The methods and modularity of the developed system allow its application to both broad areas and applications.To investigate the impact of possible land use change and urbanization, we evaluated a set of alternative future patterns of land use developed for a study area in Southwest California. Four land use and two population variants (increases of 500k and 1M) were considered. Overall, a Regional Low-Density Future was seen to have the highest pollutant emissions, largest increase in VKT, and the greatest impact on air quality. On the other hand, a Three-Centers Future appeared to be the most beneficial alternative future land-use scenario in terms of air quality. For all cases, the increase in population was the main factor leading to the change on predicted pollutant levels.     

Effects of Land Use Data on Dry Deposition in a Regional Photochemical Model for Eastern Texas

ABSTRACT

Land use data are among the inputs used to determine dry deposition velocities for photochemical grid models such as the Comprehensive Air Quality Model with extensions (CAMx) that is currently used for attainment demonstrations and air quality planning by the state of Texas. The sensitivity of dry deposition and O₃ mixing ratios to land use classification was investigated by comparing predictions based on default U.S. Geological Survey (USGS) land use data to predictions based on recently compiled land use data that were collected to improve biogenic emissions estimates. Dry deposition of O₃ decreased throughout much of eastern Texas, especially in urban areas, with the new land use data. Predicted 1-hr averaged O₃ mixing ratios with the new land use data were as much as 11 ppbv greater and 6 ppbv less than predictions based on USGS land use data during the late afternoon. In addition, the area with peak O₃ mixing ratios in excess of 100 ppbv increased significantly in urban areas when deposition velocities were calculated based on the new land use data. Finally, more detailed data on land use within urban areas resulted in peak changes in O₃ mixing ratios of ~2 ppbv. These results indicate the importance of establishing accurate, internally consistent land use data for photochemical modeling in urban areas in Texas. They also indicate the need for field validation of deposition rates in areas experiencing changing land use patterns, such as during urban reforestation programs or residential and commercial development.     

Methodology for estimating soil carbon for the forest carbon budget model of the United States, 2001

The largest carbon (C) pool in United States forests is the soil C pool. We present methodology and soil C pool estimates used in the FORCARB model, which estimates and projects forest carbon budgets for the United States. The methodology balances knowledge, uncertainties, and ease of use. The estimates are calculated using the USDA Natural Resources Conservation Service STATSGO database, with soil dynamics following assumptions based on results of site-specific studies, and area estimates from the USDA Forest Service. Forest Inventory and Analysis data and national-level land cover data sets. Harvesting is assumed to have no effect on soil C. Land use change and forest type transitions affect soil C. We apply the methodology to the southeastern region of the United States as a case study.     

Will urban expansion lead to an increase in future water pollution loads?—a preliminary investigation of the Haihe River Basin in northeastern China

Urban expansion is a major driving force changing regional hydrology and nonpoint source pollution. The Haihe River Basin, the political, economic, and cultural center of northeastern China, has undergone rapid urbanization in recent decades. To investigate the consequences of future urban sprawl on nonpoint source water pollutant emissions in the river basin, the urban sprawl in 2030 was estimated, and the annual runoff and nonpoint source pollution in the Haihe River basin were simulated. The Integrated Model of Non-Point Sources Pollution Processes (IMPULSE) was used to simulate the effects of urban sprawl on nonpoint source pollution emissions. The outcomes indicated that the urban expansion through 2030 increased the nonpoint source total nitrogen (TN), total phosphorous (TP), and chemical oxygen demand (COD) emissions by 8.08, 0.14, and 149.57 kg/km², respectively. Compared to 2008, the total nonpoint emissions rose by 15.33, 0.57, and 12.39 %, respectively. Twelve percent of the 25 cities in the basin would increase by more than 50 % in nonpoint source TN and COD emissions in 2030. In particular, the nonpoint source TN emissions in Xinxiang, Jiaozuo, and Puyang would rise by 73.31, 67.25, and 58.61 %, and the nonpoint source COD emissions in these cities would rise by 74.02, 51.99, and 53.27 %, respectively. The point source pollution emissions in 2008 and 2030 were also estimated to explore the effects of urban sprawl on total water pollution loads. Urban sprawl through 2030 would bring significant structural changes of total TN, TP, and COD emissions for each city in the area. The results of this study could provide insights into the effects of urbanization in the study area and the methods could help to recognize the role that future urban sprawl plays in the total water pollution loads in the water quality management process.     

Numerical simulation of the urban boundary layer over the complex terrain of Hong Kong

Due to the complexity of the underlying surface, urban boundary layers may exhibit very different wind-temperature field structures compared with rural areas. In this study, an urban boundary layer model with a resolution of 500 m is applied to Hong Kong, a place characterized by complex topography with high mountains and dense urban developments. Five surface land use types are considered; grass and shrub land, trees, water, old urban areas and new town developments. The urban boundary layer model is embedded into the National Center for Atmospheric Research (NCAR) Mesoscale Model, version 5 (MM5). The initial and boundary conditions are obtained from the National Centers for Environmental Prediction (NCEP)/NCAR reanalysis dataset. The modeling approach therefore takes into account both the mesoscale background field and the urban underlying surface. The model is applied to the simulation of a pollution episode in Hong Kong. Results show good agreement with meteorological data for the surface winds and temperature. The model successfully simulates the urban heat island and the occurrence of a sea–land breeze circulation, and their impact on air pollutant transport and dispersion.     

土地利用/土地覆被变化对区域生态环境的影响

土地利用／ 土地覆被变化对区域生态环境的影响是土地利用／ 土地覆被变化研究的重要内容。本文分析了土地利用／ 土地覆被变化对区域气候、土壤、水量和水质的影响。土地利用／ 土地覆被变化通过改变地表发射率、温室气体和痕量气体的含量影响区域气候;土地利用／土地覆被变化影响着能量交换、水交换、侵蚀与堆积、生物循环和作物生产等土壤主要生态过程,不同土地利用方式和土地覆被类型的空间组合影响着土壤养分的迁移规律;土地利用／ 土地覆被变化对水质的影响主要是通过非点源污染途径,许多非点源污染来源都同土地利用／土地覆被变化紧密联系。文中还探讨了由于人类不合理的土地利用造成的土壤侵蚀、土地退化、水资源短缺、海水入侵等生态环境问题。     

Spatial scale and seasonal dependence of land use impacts on riverine water quality in the Huai River basin,China

Land use pattern is an effective reflection of anthropic activities, which are primarily responsible for water quality deterioration. A detailed understanding of relationship between water quality and land use is critical for effective land use management to improve water quality. Linear mixed effects and multiple regression models were applied to water quality data collected from 2003 to 2010 from 36 stations in the Huai River basin together with topography and climate data, to characterize the land use impacts on water quality and their spatial scale and seasonal dependence. The results indicated that the influence of land use categories on specific water quality parameter was multiple and varied with spatial scales and seasons. Land use exhibited strongest association with dissolved oxygen (DO) and ammonia nitrogen (NH₃-N) concentrations at entire watershed scale and with total phosphorus (TP) and fluoride concentrations at finer scales. However, the spatial scale, at which land use exerted strongest influence on instream chemical oxygen demand (COD) and biochemical oxygen demand (BOD) levels, varied with seasons. In addition, land use composition was responsible for the seasonal pattern observed in contaminant concentrations. COD, NH₃-N, and fluoride generally peaked during dry seasons in highly urbanized regions and during rainy seasons in less urbanized regions. High proportion of agricultural and rural areas was associated with high nutrient contamination risk during spring. The results highlight the spatial scale and seasonal dependence of land use impacts on water quality and can provide scientific basis for scale-specific land management and seasonal contamination control.     

Comparison of the performances of land use regression modelling and dispersion modelling in estimating small-scale variations in long-term air pollution concentrations in a Dutch urban area

The performance of a Land Use Regression (LUR) model and a dispersion model (URBIS – URBis Information System) was compared in a Dutch urban area. For the Rijnmond area, i.e. Rotterdam and surroundings, nitrogen dioxide (NO₂) concentrations for 2001 were estimated for nearly 70 000 centroids of a regular grid of 100 × 100 m.A LUR model based upon measurements carried out on 44 sites from the Dutch national monitoring network and upon Geographic Information System (GIS) predictor variables including traffic intensity, industry, population and residential land use was developed. Interpolation of regional background concentration measurements was used to obtain the regional background. The URBIS system was used to estimate NO₂ concentrations using dispersion modelling. URBIS includes the CAR model (Calculation of Air pollution from Road traffic) to calculate concentrations of air pollutants near urban roads and Gaussian plume models to calculate air pollution levels near motorways and industrial sources. Background concentrations were accounted for using 1 × 1 km maps derived from monitoring and model calculations.Moderate agreement was found between the URBIS and LUR in calculating NO₂ concentrations (R = 0.55). The predictions agreed well for the central part of the concentration distribution but differed substantially for the highest and lowest concentrations. The URBIS dispersion model performed better than the LUR model (R = 0.77 versus R = 0.47 respectively) in the comparison between measured and calculated concentrations on 18 validation sites. Differences can be understood because of the use of different regional background concentrations, inclusion of rather coarse land use category industry as a predictor variable in the LUR model and different treatment of conversion of NO to NO₂.Moderate agreement was found between a dispersion model and a land use regression model in calculating annual average NO₂ concentrations in an area with multiple sources. The dispersion model explained concentrations at validation sites better.     

Using GIS tools to detect the land use/land cover changes during forty years in Lodhran District of Pakistan

Environmental Science and Pollution Research - Land use/land cover (LULC) change has serious implications for environment as LULC is directly related to land degradation over a period of time and...     

Assessing urban habitat quality based on specific leaf area and stomatal characteristics of Plantago lanceolata L

This study has evaluated urban habitat quality by studying specific leaf area (SLA) and stomatal characteristics of the common herb Plantago lanceolata L. SLA and stomatal density, pore surface and resistance were measured at 169 locations in the city of Gent (Belgium), distributed over four land use classes, i.e., sub-urban green, urban green, urban and industry. SLA and stomatal density significantly increased from sub-urban green towards more urbanised land use classes, while the reverse was observed for stomatal pore surface. Stomatal resistance increased in the urban and industrial land use class in comparison with the (sub-) urban green, but differences between land use classes were less pronounced. Spatial distribution maps for these leaf characteristics showed a high spatial variation, related to differences in habitat quality within the city. Hence, stomatal density and stomatal pore surface are assumed to be potentially good bio-indicators for urban habitat quality.     

Influence of land use on metal concentrations in playa sediments and amphibians in the Southern High Plains

The Southern High Plains (SHP) is a semi-arid region in which playa wetlands are the focal points of biodiversity. Playas are highly influenced by surrounding land use. Most of the SHP is in agricultural production (primarily cotton) with a history of arsenic-containing herbicide use. Metals influence reproduction and development in amphibians. We analyzed metal residues in playa sediment and whole body tissue of Spea spp. and Bufo cognatus metamorphs from two land uses: cropland and native grassland. Cd and Ni concentrations in B. cognatus tissues differed between land uses. Metal concentrations in Spea spp. tissues did not differ between land uses. Ba was higher in Spea spp. than B. cognatus collected from the same grassland playas, indicating differential habitat use. No correlations between sediment and tissue concentrations were found. Land use appeared to have little influence on metal concentrations and levels were below those known to cause effects in amphibians.     

Coupling air quality and land use modeling within an optimization framework

Land use regulations and air quality standards can be effective tools to control air pollution. Atmospheric transport/chemistry simulation models could be used to develop suitable regulations and standards; however, these models are not as efficient as air quality management models developed from embedding governing equations for atmospheric transport/chemistry into an optimization framework. Formulations of two steady-state air quality management models are presented to facilitate the development or evaluation of land use strategies to protect regional air quality from pollution generated from distributed point or nonpoint sources. Both models are linear programs constructed with equations that describe steady-state atmospheric pollutant fate and transport. The first model determines feasible pollutant loading patterns for multiple land use activities to accommodate the greatest regional population. The second model ascertains patterns of expanded land use which have a minimum impact on air quality. The primary goal of this paper is to explain how air pollution and land use modeling may be coupled to create an effective management tool to aid scientists and engineers with decisions affecting air quality and land use. The secondary goal is to show the types of air quality and regulatory information which could be obtained from these models. This latter goal is attained with general conclusions as consequence of applying ‘duality theory.’     

The use of wind fields in a land use regression model to predict air pollution concentrations for health exposure studies

A methodology is developed to include wind flow effects in land use regression (LUR) models for predicting nitrogen dioxide (NO₂) concentrations for health exposure studies. NO₂ is widely used in health studies as an indicator of traffic-generated air pollution in urban areas. Incorporation of high-resolution interpolated observed wind direction from a network of 38 weather stations in a LUR model improved NO₂ concentration estimates in densely populated, high traffic and industrial/business areas in Toronto-Hamilton urban airshed (THUA) of Ontario, Canada. These small-area variations in air pollution concentrations that are probably more important for health exposure studies may not be detected by sparse continuous air pollution monitoring network or conventional interpolation methods. Observed wind fields were also compared with wind fields generated by Global Environmental Multiscale-High resolution Model Application Project (GEM-HiMAP) to explore the feasibility of using regional weather forecasting model simulated wind fields in LUR models when observed data are either sparse or not available. While GEM-HiMAP predicted wind fields well at large scales, it was unable to resolve wind flow patterns at smaller scales. These results suggest caution and careful evaluation of regional weather forecasting model simulated wind fields before incorporating into human exposure models for health studies. This study has demonstrated that wind fields may be integrated into the land use regression framework. Such integration has a discernable influence on both the overall model prediction and perhaps more importantly for health effects assessment on the relative spatial distribution of traffic pollution throughout the THUA. Methodology developed in this study may be applied in other large urban areas across the world.     

Modeling and spatially distributing forest net primary production at the regional scale

Forest, agricultural, rangeland, wetland, and urban landscapes have different rates of carbon sequestration and total carbon sequestration potential under alternative management options. Changes in the proportion and spatial distribution of land use could enhance or degrade that area's ability to sequester carbon in terrestrial ecosystems. As the ecosystems within a landscape change due to natural or anthropogenic processes, they may go from being a carbon sink to a carbon source or vice versa. Satellite image analysis has been tested for timely and accurate measurement of spatially explicit land use change and is well suited for use in inventory and monitoring of terrestrial carbon. The coupling of Landsat Thematic Mapper (TM) data with a physiologically based forest productivity model (PnET-II) and historic climatic data provides an opportunity to enhance field plot-based forest inventory and monitoring methodologies. We use periodic forest inventory data from the U.S. Department of Agriculture (USDA) Forest Service's Forest Inventory and Analysis (FIA) Program to obtain estimates of forest area and type and to generate estimates of carbon storage for evergreen, deciduous, and mixed-forest classes. The area information is used in an accuracy assessment of remotely sensed forest cover at the regional scale. The map display of modeled net primary production (NPP) shows a range of forest carbon storage potentials and their spatial relationship to other landscape features across the southern United States. This methodology addresses the potential for measuring and projecting forest carbon sequestration in the terrestrial biosphere of the southern United States.     

Distribution and sources of polycyclic aromatic hydrocarbons from urban to rural soils: a case study in Dalian, China

To estimate the distribution and sources of soil polycyclic aromatic hydrocarbons (PAHs) in metropolitan and adjacent areas, soil samples were collected from urban, suburban and rural locations of Dalian, China, and concentrations of 14 PAHs were determined. The spatial PAH profiles were site-specific and determined by the sources close to the sampling sites. PAH concentrations decreased significantly along the urban-suburban-rural transect. The gradient implied that the fractionation effect influenced PAH distribution. Bivariate plots of selected diagnostic ratios showed general trends of co-variation and allowed to distinguish samples taken from different areas. An improved method, factor analysis (FA) with nonnegative constrains, was used to determine the primary sources and contributions of PAHs in soils. The FA model showed traffic average (74%) and coal related residential emission (26%) were two primary sources to Dalian soils. In addition, the FA model provided reasonable explanations for PAH contributions in soils from different sites. The results suggest that FA with nonnegative constraints is a promising tool for source apportionment of PAHs in soils.     

Resolving the interactions between population density and air pollution emissions controls in the San Joaquin Valley, USA

The effectiveness of emissions control programs designed to reduce concentrations of airborne particulate matter with an aerodynamic diameter < 2.5 microm (PM2.5) in California's San Joaquin Valley was studied in the year 2030 under three growth scenarios: low, medium, and high population density. Base-case inventories for each choice of population density were created using a coupled emissions modeling system that simultaneously considered interactions between land use and transportation, area source, and point source emissions. The ambient PM2.5 response to each combination of population density and emissions control was evaluated using a regional chemical transport model over a 3-week winter stagnation episode. Comparisons between scenarios were based on regional average and population-weighted PM2.5 concentrations. In the absence of any emissions control program, population-weighted concentrations of PM2.5 in the future San Joaquin Valley are lowest undergrowth scenarios that emphasize low population density. A complete ban on wood burning and a 90% reduction in emissions from food cooking operations and diesel engines must occur before medium- to high-density growth scenarios result in lower population-weighted concentrations of PM2.5. These trends partly reflect the fact that existing downtown urban cores that naturally act as anchor points for new high-density growth in the San Joaquin Valley are located close to major transportation corridors for goods movement. Adding growth buffers around transportation corridors had little impact in the current analysis, since the 8-km resolution of the chemical transport model already provided an artificial buffer around major emissions sources. Assuming that future emissions controls will greatly reduce or eliminate emissions from residential wood burning, food cooking, and diesel engines, the 2030 growth scenario using "as-planned" (medium) population density achieves the lowest population-weighted average PM2.5 concentration in the future San Joaquin Valley during a severe winter stagnation event. Implications: The San Joaquin Valley is one of the most heavily polluted air basins in the United States that are projected to experience strong population growth in the coming decades. The best plan to improve air quality in the region combines medium- or high-density population growth with rigorous emissions controls. In the absences of controls, high-density growth leads to increased population exposure to PM2.5 compared with low-density growth scenarios (urban sprawl).     

Life cycle implications of urban green infrastructure

Low Impact Development (LID) is part of a new paradigm in urban water management that aims to decentralize water storage and movement functions within urban watersheds. LID strategies can restore ecosystem functions and reduce runoff loadings to municipal water pollution control facilities (WPCF). This research examines the avoided energy and greenhouse gas (GHG) emissions of select LID strategies using life cycle assessment (LCA) and a stochastic urban watershed model. We estimate annual energy savings and avoided GHG emissions of 7.3 GJ and 0.4 metric tons, respectively, for a LID strategy implemented in a neighborhood in New York City. Annual savings are small compared to the energy and GHG intensity of the LID materials, resulting in slow environmental payback times. This preliminary analysis suggests that if implemented throughout an urban watershed, LID strategies may have important energy cost savings to WPCF, and can make progress towards reducing their carbon footprint.     

Modelling the influence of peri-urban trees in the air quality of Madrid region (Spain)

Tropospheric ozone (O(3)) is considered one of the most important air pollutants affecting human health. The role of peri-urban vegetation in modifying O(3) concentrations has been analyzed in the Madrid region (Spain) using the V200603par-rc1 version of the CHIMERE air quality model. The 3.7 version of the MM5 meteorological model was used to provide meteorological input data to the CHIMERE. The emissions were derived from the EMEP database for 2003. Land use data and the stomatal conductance model included in CHIMERE were modified according to the latest information available for the study area. Two cases were considered for the period April-September 2003: (1) actual land use and (2) a fictitious scenario where El Pardo peri-urban forest was converted to bare-soil. The results show that El Pardo forest constitutes a sink of O(3) since removing this green area increased O(3) levels over the modified area and over down-wind surrounding areas.