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Spatial disaggregation of carbon dioxide emissions from road traffic based on multiple linear regression model
Atmospheric Environment 45 (2011) 634e640
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Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Spatial disaggregation of carbon dioxide emissions from road traffic based on multiple linear regression model Yuqin Shu a, *, Nina S.N. Lam b,1 a b
School of Geographical Science, South China Normal University, Shipai, Guangzhou 510631, China Department of Environmental Sciences, Louisiana State University, Baton Rouge 70820, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 5 May 2010 Received in revised form 22 October 2010 Accepted 22 October 2010
Detailed estimates of carbon dioxide emissions at fine spatial scales are critical to both modelers and decision makers dealing with global warming and climate change. Globally, traffic-related emissions of carbon dioxide are growing rapidly. This paper presents a new method based on a multiple linear regression model to disaggregate traffic-related CO2 emission estimates from the parish-level scale to a 1 1 km grid scale. Considering the allocation factors (population density, urban area, income, road density) together, we used a correlation and regression analysis to determine the relationship between these factors and traffic-related CO2 emissions, and developed the best-fit model. The method was applied to downscale the traffic-related CO2 emission values by parish (i.e. county) for the State of Louisiana into 1-km2 grid cells. In the four highest parishes in traffic-related CO2 emissions, the biggest area that has above average CO2 emissions is found in East Baton Rouge, and the smallest area with no CO2 emissions is also in East Baton Rouge, but Orleans has the most CO2 emissions per unit area. The result reveals that high CO2 emissions are concentrated in dense road network of urban areas with high population density and low CO2 emissions are distributed in rural areas with low population density, sparse road network. The proposed method can be used to identify the emission “hot spots” at fine scale and is considered more accurate and less time-consuming than the previous methods. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Multiple linear regression model Carbon dioxide emissions Spatial disaggregation
1. Introduction Carbon dioxide (CO2) emissions are produced everyday through burning of fossil fuels essential to our basic needs such as electricity, heating, and transportation. Increased CO2 emissions in the last few decades have been suggested to contribute to global warming. Road vehicles are acknowledged to be significant sources. As such, road traffic related emissions of CO2 have received special attention. Emissions reduction is thus probably the most important goals of policy on both climate change and air pollution. There are ways for reducing CO2 emissions, such as better transport infrastructure and advances in vehicle management systems. For an example, the UK government has identified spatial hydrogen infrastructures and technologies as potentially playing a major role, notably in the transport sector for a 60% CO2 reduction by 2050 (Strachan et al., 2009). However, if these reduction strategies are established efficiently for both the science and policy-making
* Corresponding author. Tel.: þ86 20 8521 5950; fax: þ86 20 8521 3620. E-mail addresses: [email protected] (Y. Shu), [email protected] (N.S.N. Lam). 1 Tel.: þ1 225 578 3030; fax: þ1 225 5784286. 1352-2310/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2010.10.037
communities, the amount and spatial distribution of CO2 emissions at high resolution should be determined in advance, which could help them find hot spots or vulnerable spots directly in specific area. Spatial disaggregation (allocation) is a top-down process to transform large and irregularly shaped emission data to relatively small and uniform (usually rectangular) data (Streets et al., 2003). The top-down disaggregation is based on the assumption that they are highly correlated with some indicators or factors (population, land use, etc.). There have been a few attempts in spatially disaggregating traffic emissions. There were approximately 65 spatial surrogates (allocation factors) in U.S. EPA plans to use for modeling the 1999 and derived inventories, or use with Sparse Matrix Operator Kernel Emissions (SMOKE) processing system for a specified 4 km grid, a 12 km grid and a 36 km grid (U.S. Energy Protection Agency (EPA), 2005). Urban road length, rural road length and total road length were used to allocate onroad emissions to a grid cell. In the United States, a project named “Vulcan” has quantified fossil fuel CO2 emissions for the contiguous U.S. at spatial scales of less than 10 10 km grid and temporal scales as fine as hours (Gurney et al., 2009a,b). The Vulcan inventory data for the year 2002 was constructed from
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seven emission data sets, which come in different forms (e.g., point or area form) and at different spatial scales (e.g., facility or parish level). Then onroad mobile emissions were downscaled from the parish level by allocating the hourly/parish/road/vehicle-specific CO2 onto roadways using a GIS road atlas with an underlying assumption that road traffic CO2 emissions are related to the road length and road class. There were important studies in South America (Chile specifically) aimed at obtaining traffic emissions. A spatial disaggregation approach was developed in mid-sized Chilean urban area using principal roads density (Osses de Elcker et al., 2008; Tuia et al., 2007), which gave the 1-km2 spatial distribution of hot emissions. Traffic count classification, land use, both combined with a simplified road network were regarded as surrogates or proxies in a large city (Santiago, the capital city of Chile) for deriving the 1-km2 spatial emissions (Saide et al., 2009). An inventory of air pollutant in Asia in the year 2000 was developed (Streets et al., 2003), in which a ‘‘top-down’’ approach was used to break regional- or country-based emissions data down to a variety of spatial resolutions from 1 1 to 30 s 30 s. Line sources emission allocation was conducted by multiplying pregenerated allocation factors by regional or national emission total. The allocation factors for line sources (road networks) were created by a combination and transformation of geographical information, such as road network, population distribution, and landcover classes. In the case of Europe, there was a three-tiered methodology for estimating emissions on EMEP (European Monitoring and Evaluation Programme) 50 50 km grid (European Environment Agency, 2009). First, national transport emissions may be allocated to road links based on measured or modeled traffic flow. Second, national transport emissions may be allocated using road network information and population based traffic intensity. Third, national transport emissions may be allocated also only by spatial surrogate (e.g. Population and land cover). Where appropriate traffic flow data are not available, emission distributions can be generated using digital road maps and population density data. For example, the APMoSPHERE project provided the emission estimates at 1 km resolution across the whole EU using a method, which was based on the assumption that population density was related to number of vehicles using different types of road network(highways, urban roads and rural roads). Then the road network was determined to be weighted according to the contribution to emissions in specific area (Briggs, 2005; Vienneau et al., 2009). These top-down disaggregation approaches to spatially allocate emission sources from a coarse geographic area to finer grid cells have proven to be reasonably accurate in emission estimates. The top-down approach usually can be roughly divided into two main steps: 1) allocation factors were supposed to correlate with emissions; 2) the coarse-scale estimates were allocated proportionally into fine-scale estimates. However, there are three shortcomings regarding the previous downscaling approaches. Firstly, the specific contribution of each allocation factor to total emissions was ignored. Only used was the ratio (percentage, proportion) of the amount of allocation factor in a grid cell to the total amount in a coarse geographic area. Grid cell emissions were calculated by multiplying the allocation factor’s ratio of grid cell by coarse-scale (e.g. parish) emissions. For example, in Vulcan project, the road segments were split by the 10 10 km grid cells, the grid cell emissions were calculated by multiplying the percentage of its original length by the original segment’s total emissions. The second shortcoming of most previous disaggregation approaches is that they have seldom incorporated population, income and urban area factors with road condition together in allocating emission estimates. Therefore, the emission estimates at finer scale were
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probably underestimated in high population density zone, urban center with high income. Likewise, those in low population density zone, like rural area, were probably overestimated. For example, only a simplified road network was used as an allocation factor for deriving spatial patterns of emissions in Chile (Tuia et al., 2007), which resulted in underestimation of emissions in urban centers, as well as overestimation in residential zones. Even though the APMoSPHERE project considered a few of allocation factors (population, land cover with road network) to broke these emissions down to 1 1 km grid cells, improvements also need to be made for modeling among emissions and these factors if the estimates are to be updated regularly. Obviously, it is necessary to build a multiple linear regression model to determine the relationship between allocation factors and traffic-related CO2 emissions. Currently, there is a remarkable tendency in the different countries towards the development of high-resolution emission estimates (1-km2) needed for policy-making on climate change and air pollution (Baldasano et al., 2008; Osses de Elcker et al., 2008; Saide et al., 2009; Tuia et al., 2007; Vienneau et al., 2009). And in order to overcome the two shortcomings of previous approaches, this paper proposed a new method based on a multiple linear regression model to perform spatial disaggregation of trafficrelated CO2 emissions from the parish-level scale to a 1-km2 grid scale. The State of Louisiana was chosen for the application. 2. Study area Situated in the south of the United States, the State of Louisiana was the 10th highest carbon dioxide emission state in the U.S. (U.S. Energy Information Administration (EIA), 2008a). The average percapita emission of Louisiana was approximately 40 tons of CO2 each year. CO2 emissions were inventoried by five sectors (U.S. Energy Information Administration (EIA), 2008b). The industrial sector accounted for 46 percent of all CO2 emissions from combustion of fossil fuels in 2005. The next two largest sources of carbon dioxide were the transportation sector (28 percent) and electric utilities (23 percent). The combined share of residential and commercial sectors was fairly small, approximately 3 percent (U.S. Energy Information Administration (EIA), 2008c). CO2 emissions data from the transportation sector for the year 2002 at the parish scale in Louisiana were obtained from the Vulcan inventory. The emission estimates contained three separate components: on-road emissions (mobile transport using designated roadways), non-road emissions (vessels along waterways, trains along railroads), and airport emissions associated with air travel (Gurney et al., 2009a). The CO2 emissions from road traffic took up 80.5% in Louisiana, and emissions from the other two components took up approximately 19.5%. There are 64 parishes in Louisiana, East Baton Rouge, Orleans, Jefferson, Caddo Parish are the four highest ones in CO2 emissions from road traffic. As background information for onroad emissions, according to the statistics in 2000 provided by the Louisiana Department of Transportation and Development (LDOTD), Louisiana had a total of 60,765 miles of road, 1,964,694 registered cars and 1,570,804 registered trucks. 3. Multiple linear regression model Regression analysis is widely used for prediction and forecasting. Multiple linear regression attempts to model the relationship between two or more explanatory variables and a response variable by fitting a linear equation to observed data. In this paper, we determined the correlation relationship between the allocation factors and CO2 emissions from road traffic with the average values per unit area (1-km2) at parish scale.
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Table 1 Data description and sources. Data
Form
Description
Coordinate system
Source
Date
CO2 emissions Income Parish Basic cell Road
Attribute Attribute Polygon Polygon Polyline
None None Geographic NAD83 Geographic NAD83 Geographic NAD83
Vulcan Inventory BEA of U. S. Census TIGER This project Census TIGER
2002 2002 2009 2010 2006
Urban area Population
Polygon Grid
On-road in each parish Income per person in each parish Boundaries of parishes 1 km 1 km Centerlines of road (Interstate, U.S, State and county highway, local, neighborhood and rural road, etc.) Based on the latest available governmental unit boundaries 30 s 30 s
Geographic NAD83 Geographic WGS 1984
Census TIGER LandScan
2006 2006
3.1. Allocation factors Selecting factors is the key to build the multiple linear regression model. Firstly, there have been a number of studies aimed at analyzing the factors that influence CO2 emissions. The IPAT model took the form of an equation combining environmental impact (I) with population size (P), affluence (A), and the level of environmentally damaging technology (T), known as I ¼ PAT. It’s still used for analyzing the driving forces of environmental change (York et al., 2002). The STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model is a stochastic model based on IPAT to estimate the effects of human population, affluence, technology on environment (York et al., 2003). CO2 emissions are produced when carbon-based fuels are burned. Energy consumption leads to CO2 emissions. Ordinarily, the more the population is, the more energy consumption there is. It is found that (Schafer and Victor, 1999) people increase their mobility, go longer distances when incomes rise. Thus, they use more energy and emit more CO2. It is believed that CO2 emissions are higher in the denser areas of towns (Reckien et al., 2007). There are three major factors that could significantly influence energy consumption, namely population growth, economic growth and high urbanization (Kazim, 2007; Liu, 2009). Secondly, in the transportation sector, the relationship between transport structure and CO2 emissions from road traffic has long been recognized (Newman and Kenworthy, 1989). Studies mainly
focused on transport infrastructure such as length and width of roads, speed allowance, supply of car parks/parking spaces and car ownership, as well as the availability, reliability, frequency of and distance to public transport. But such detailed traffic data at high resolution are very limited and difficult to obtain for a large study area (i.e. the State of Louisiana). Road length or road density have been used widely as a surrogate to allocate emissions in previous studies (Gurney et al., 2009b; Osses de Elcker et al., 2008; Saide et al., 2009; Streets et al., 2003; Tuia et al., 2007). Road density is computed by dividing the total road length within a geographic area by its area. Hence, we considered road density as an allocation factor to indicate transport structure. Therefore, population density, income, urban area and road density are regarded as allocation factors in this paper, which play different roles in explaining the amount of CO2 emissions from road traffic.
3.2. Data sources and data processing There are two groups of data. The first group is for building the multiple regression model between independent variables (population density, urban area, income and road density) and dependent variable (CO2 emissions) at parish scale. The second group is for disaggregating the CO2 emissions at 1-km2 grid cell. A number of spatial data or non-spatial data were required and were obtained for the project (see Table 1).
Fig. 1. The main steps involved in spatial data processing.
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Since only CO2 emissions and income value at parish scale were obtained respectively from the Vulcan inventory and the Bureau of Economic Analysis (BEA) of U.S. Department of Commerce, the other data in the first group should be processed in advance. The main spatial data processing is summarized in Fig. 1. It involves a number of GIS functions, including converting grids to points, intersecting, calculating geometric property, spatial join and summarization. These GIS functions are critical to this study. The procedures were carried out under the ArcGIS 9.2 platform. 3.2.1. Population density Firstly, the module of converting grids to points is needed to convert the LandScanTM Dataset developed by Oak Ridge National Laboratory (Bhaduri et al., 2007) into points. Each point indicates population number. Secondly, points are allocated directly to a parish within which they are contained by intersecting the points with parish boundaries. Thirdly, spatial join and summarization are used to give a summary of population of that points which fall inside it. Population density is computed by dividing population number by parish’s area. 3.2.2. Urban area An urbanized area percentage is used to reflect urbanization level. Firstly, a new dataset of new urban areas (polygons) contained within each parish is produced by intersecting the urban areas with parish boundaries. Secondly, the polygon’s areas are recalculated with geometrical attribute. Thirdly, spatial join and summarization are also used to give a summary of areas of that urban which fall inside it. The urbanized area percentage is computed by dividing the new urban area by parish’s area. 3.2.3. Road density Intersecting the roads with parish boundaries would create a new dataset of shorter roads length contained within each parish. The new roads lengths are computed again with calculating geometry function. Spatial join and summarization are used to give a summary of length of roads which fall inside it. Then road density is computed by dividing the total roads length by parish’s area. The second group data was obtained as the above way. All data in the first group has been divided by parish’s area, so we obtained the average values of per unit area (1-km2). And the unit is in accordance with that of the second group data. Although the regression model is built at the parish scale, the first group data to form the regression model is the value of unit area. So, in this sense, the scale can also be considered to be based on 1-km2. Therefore, it’s assumed to be reasonable and valid that we estimated CO2 emissions at 1 1 km grid cells with the second group data using a regression equation, which’s built with representative and average data sets of 64 parishes in the first group. 3.3. Regression analysis We attempted to model the relationship between the four independent variables and a dependent variable by fitting a linear Table 2 Correlations among CO2 emissions, population density, income, urban area and road density.
Pearson correlation CO2 emissions Population density Income Urban area Road density
CO2 emissions
Population density
Income
Urban area
Road density
1.000
0.956
0.575
0.903
0.835
0.956 0.575 0.903 0.835
1.000 0.512 0.815 0.766
0.512 1.000 0.583 0.589
0.815 0.583 1.000 0.765
0.766 0.589 0.765 1.000
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Table 3 Statistical performance indicators of different models.d Model
a
1 2 3 a b c d
R 0.956 0.980b 0.983c
R2
Adjusted R2
Std. error of the estimate
0.915 0.960 0.967
0.913 0.959 0.966
23.987 16.562 15.097
Predictors: (constant), Population density. Predictors: (constant), Population density, Urban area. Predictors: (constant), Population density, Urban area, Road density. Dependent Variable: CO2 emissions.
equation to observed data with multiple linear regression model, which is set as follows:
E ¼ b0 þ bp P þ bi I þ bu U þ br R
(1)
where the dependent variable E is total CO2 emissions (tons year1 km2); P is population density (persons km2); U is urbanization area (%); I is annual income per person (dallors km2); R is road density (km1). b0, bp, bi, bu, br are partial regression coefficients. We used a correlation and regression analysis of the first group data of the 64 parishes in the State of Louisiana (in section 3.2). Correlations between CO2 emissions and allocation factors can be found in Table 2. The analysis is based on the Pearson correlation coefficient. The correlation coefficients are all positive and significances of (one-tailed test) are zero, which suggests a significant correlation to the dependent variable (CO2 emissions). With the exception of income factor, all other factors have very high correlation with CO2 emissions. Since stepwise selection of the variables allows dropping or adding variables at the various steps in either direction, it couldn’t happen that any significant variables are dropped or non-significant variables are added in model. Therefore, a stepwise selection method was chosen, which reiterates the analysis by each parameter in turn and independently considers the inclusion or exclusion of the parameters with every step (criteria: probability-of-F-toenter <¼ 0.05, probability-of-F-to-remove >¼ 0.1). Although Eq. (1) includes all the allocation factors, not all of these factors may be statistically significant. The income factor with the largest probability of F is removed. Model 1 is the simplest equation including only population density variable. Then the impact from urban area is added in Model 2, which explanation capacity is improved (R2 ¼ 0.960 > 0.915). The explanation capacity of Model 3 is improved slightly (R2 ¼ 0.967), but the standard error of the estimate is reduced (SEE ¼ 15. 097 < 16.562) when the impact of road density is added. By comparison with other models preformed, Model 3 (including population density, urban area, and road density) should be regarded as the best one, which means that the cumulative effect of the three factors on CO2 emissions is significant (see Table 3). The regression model (Eq. (2)) shows that the associated F-Test statistics for overall is 590.841 and significance is 0.000. T-test significances of all three allocation factors are less than 0.05 (see Table 4). It can Table 4 Coefficients of model. Model 3
(Constant) Population density Urban area Road density Total model
Unstandardized coefficients
Standardized coefficients
B
Std. error
Beta
2.165 0.547 312.502 16.439
5.483 0.040 44.280 4.487
0.596 0.307 0.144
T or F
Sig.
0.395 13.664 7.057 3.664
0.694 0.000 0.000 0.001
590.841
0.00
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Fig. 2. Spatial disaggregation of traffic-related CO2 emissions from parish scale to 1 1 km grids in the U.S. State of Louisiana.
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significantly impact a prediction in CO2 emissions from road traffic when population density, urban area, road density are taken into account. The following is the regression model that we obtained for the given data:
E ¼ 2:165 þ 0:547P þ 312:502U þ 16:439R
(2)
4. Results and discussions We used the estimated coefficients of the multiple linear regression model (Eq. (2)) to project CO2 emissions from road traffic at 1 1 km gird for the year 2002. Thus, Total amount of emissions at parish level had been disaggregated with the second group data of Louisiana (in section 3.2). The current method has involved 135,164 1 1 km grid cells for the State of Louisiana. The resolution is sufficient to provide information regarding CO2 emission patterns on a finer scale. Fig. 2 shows the main steps of spatial disaggregation of trafficrelated CO2 emissions from parish scale to 1-km 2 grid cells. At the parish level, the four highest CO2 emission parishes in Louisiana are East Baton Rouge, Orleans, Jefferson and Caddo Parish, which had emission values higher than 350,000 tons per year in 2002 (Fig. 2 (a)). Thereby the four parishes were chosen to show distribution pattern of CO2 emissions at 1-km2 gird cells after spatial disaggregation (Fig. 2(c)). In the four highest parishes, the mean gird value is 219 tons. There are 505 grids that are above the mean grid CO2 emission in East Baton Rouge, 202 grids in Orleans, 296 girds in Jefferson, 330 grids in Caddo. There are 167 grids that have no traffic-related emissions in East Baton Rouge, 512 grids in Orleans, 1209 grids in Jefferson, 357 grids in Caddo. The highest grid value is 6929 tons in Orleans parish, 5630 tons in East Baton Rouge, 4785 tons in Jefferson, 2904 tons in Caddo. We could conclude that East Baton Rouge has the biggest area in which emitted traffic-related CO2 emissions exceeds the average. It also has the smallest area with no traffic-related emissions. It is Orleans that has the most CO2 emissions per 1-km2. Comparing the original traffic-related CO2 emissions by parish with the 1-km2 grid estimates, the gridded map shows variation within a parish, which allows the identification of the emission “hot spots”. There are some hot spots identified in the gridded map (Fig. 2 (c)), Baton Rouge in East Baton Rouge Parish, Shreveport in Caddo Parish, New Orleans in Orleans parish, Gretna, Harvey, Kenner, Marrero, Metairie, Terrytown and so on in Jefferson Parish. Other parishes of interest can also be disaggregated spatially in the same way. The method proposed in this paper is based on the assumption that population density, road density, urban area, income have effects on traffic-related CO2 emissions. As the highest CO2 emission parish, East Baton Rouge was selected to present spatial distribution of selected allocation factors at 1-km2 grid cells (Fig. 2 (b)). The result (Fig. 2(b) and (c)) reveals that high CO2 emissions are concentrated in dense road network of urban areas with high population density and low CO2 emissions are distributed in rural areas with low population density, sparse road network. Compared with the previous studies at 1-km2 grid scale (Tuia et al., 2007), it could minimize underestimation in the main urban area, high loaded road network, or in high population density area. Also it could minimize overestimation of emissions in rural region. A multiple linear regression model in this paper was built to determine specific contribution of each allocation factor to total emissions, so that it’s less time-consuming to calculate or update emission estimates regularly than those methods without regression models (Briggs, 2005; Vienneau et al., 2009). In the light of the standardized beta values for all three factors, it tells us that population has greatest impact on CO2 emissions, and
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road density has least impact in Model 3 (see Table 4). The most important limitation of the method in this paper is that only road density is determined to represent the traffic network for neglecting differences in traffic flow and speed of vehicles. Thereby, the method is not able to simulate the real road traffic situation. But we extremely recommend that a spatial redistribution that takes into account detailed traffic data would be worth being carried out in hot spot once it’s identified by the method proposed in this paper. We also acknowledge that there is still uncertainty in the emission estimates at grid cell level because of the error or gaps in the original data. The spatial uncertainty in this method probably resulted from spatial data process too. For an example, minor errors were expected during the conversion from population grids to points and overlay analysis between parish boundaries or grid cells and population points. Because estimates accuracy is affected by the model, we would monitor CO2 emissions at different sites or representative sample of locations within the state to evaluate and improve our model for estimates accuracy in the future research. 5. Conclusions Basically, we applied a top-down approach based on a multiple linear regression model to break parish-based emission data down to 1-km2 gird cells, using the State of Louisiana as an example. Considering the allocation factors (population density, urban area, income, and road density) together, we used a correlation and regression analysis to determine the relationship between these factors and traffic-related CO2 emissions. The best-fit model was developed using stepwise selection method, which included population density, urban area, and road density, and excluded income factor because of the largest probability of F. We built the model with the average values per unit area (1-km2) at parish scale and employed it to calculate the emissions at 1 1 km grid cells in the study area. Therefore, traffic-related CO2 emissions can be faithfully allocated “down” to each grid cell. The resultant grid estimates derived from the proposed method hence are considered more accurate and less time-consuming than the previous methods. At the same time, the method is very flexible such that different assumptions or different factors can be incorporated in future studies. For example, more factors could be taken into account in improving the multiple linear regression model, such as the road type, road lanes, and traffic volumes and so on. The data will serve as a critical input to many energy or climate impact modeling efforts and provide a baseline dataset for policy development and decision making. References Baldasano, J.M., Guereca, L.P., Lopez, E., Gasso, S., Jimenez-Guerrero, P., 2008. Development of a high-resolution (1 km 1 km, 1 h) emission model for Spain: the high-elective resolution modelling emission system (HERMES). Atmospheric Environment 42 (31), 7215e7233. Bhaduri, B., Bright, E., Coleman, P., Urban, M., 2007. LandScan USA: a high resolution geospatial and temporal modeling approach for population distribution and dynamics. GeoJournal 69, 103e117. Briggs, D., 2005. APMoSPHERE (Air Pollution Modelling for Support to Policy on Health, Environment and Risk Management in Europe). Imperial College of Science, Technology and Medicine, London. European Environment Agency, 2009. EMEP/EEA air pollutant emission inventory guidebook-2009. EEA Technical Report. Gurney, K.R., Mendoza, D., Geethakumar, S., Zhou, Y., Miller, C., Sahni, N., Seib, B., Ansley, W., Fischer, M., Du Can, S.D., 2009a. Vulcan Science Methods Documentation, Version 1.1. Gurney, K.R., Mendoza, D.L., Zhou, Y.Y., Fischer, M.L., Miller, C.C., Geethakumar, S., Du Can, S.D., 2009b. High resolution fossil fuel combustion CO2 emission fluxes for the United States. Environmental Science & Technology 43 (14), 5535e5541. Kazim, A.M., 2007. Assessments of primary energy consumption and its environmental consequences in the United Arab Emirates. Renewable & Sustainable Energy Reviews 11 (3), 426e446.
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Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Spatial disaggregation of carbon dioxide emissions from road traffic based on multiple linear regression model Yuqin Shu a, *, Nina S.N. Lam b,1 a b
School of Geographical Science, South China Normal University, Shipai, Guangzhou 510631, China Department of Environmental Sciences, Louisiana State University, Baton Rouge 70820, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 5 May 2010 Received in revised form 22 October 2010 Accepted 22 October 2010
Detailed estimates of carbon dioxide emissions at fine spatial scales are critical to both modelers and decision makers dealing with global warming and climate change. Globally, traffic-related emissions of carbon dioxide are growing rapidly. This paper presents a new method based on a multiple linear regression model to disaggregate traffic-related CO2 emission estimates from the parish-level scale to a 1 1 km grid scale. Considering the allocation factors (population density, urban area, income, road density) together, we used a correlation and regression analysis to determine the relationship between these factors and traffic-related CO2 emissions, and developed the best-fit model. The method was applied to downscale the traffic-related CO2 emission values by parish (i.e. county) for the State of Louisiana into 1-km2 grid cells. In the four highest parishes in traffic-related CO2 emissions, the biggest area that has above average CO2 emissions is found in East Baton Rouge, and the smallest area with no CO2 emissions is also in East Baton Rouge, but Orleans has the most CO2 emissions per unit area. The result reveals that high CO2 emissions are concentrated in dense road network of urban areas with high population density and low CO2 emissions are distributed in rural areas with low population density, sparse road network. The proposed method can be used to identify the emission “hot spots” at fine scale and is considered more accurate and less time-consuming than the previous methods. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Multiple linear regression model Carbon dioxide emissions Spatial disaggregation
1. Introduction Carbon dioxide (CO2) emissions are produced everyday through burning of fossil fuels essential to our basic needs such as electricity, heating, and transportation. Increased CO2 emissions in the last few decades have been suggested to contribute to global warming. Road vehicles are acknowledged to be significant sources. As such, road traffic related emissions of CO2 have received special attention. Emissions reduction is thus probably the most important goals of policy on both climate change and air pollution. There are ways for reducing CO2 emissions, such as better transport infrastructure and advances in vehicle management systems. For an example, the UK government has identified spatial hydrogen infrastructures and technologies as potentially playing a major role, notably in the transport sector for a 60% CO2 reduction by 2050 (Strachan et al., 2009). However, if these reduction strategies are established efficiently for both the science and policy-making
* Corresponding author. Tel.: þ86 20 8521 5950; fax: þ86 20 8521 3620. E-mail addresses: [email protected] (Y. Shu), [email protected] (N.S.N. Lam). 1 Tel.: þ1 225 578 3030; fax: þ1 225 5784286. 1352-2310/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2010.10.037
communities, the amount and spatial distribution of CO2 emissions at high resolution should be determined in advance, which could help them find hot spots or vulnerable spots directly in specific area. Spatial disaggregation (allocation) is a top-down process to transform large and irregularly shaped emission data to relatively small and uniform (usually rectangular) data (Streets et al., 2003). The top-down disaggregation is based on the assumption that they are highly correlated with some indicators or factors (population, land use, etc.). There have been a few attempts in spatially disaggregating traffic emissions. There were approximately 65 spatial surrogates (allocation factors) in U.S. EPA plans to use for modeling the 1999 and derived inventories, or use with Sparse Matrix Operator Kernel Emissions (SMOKE) processing system for a specified 4 km grid, a 12 km grid and a 36 km grid (U.S. Energy Protection Agency (EPA), 2005). Urban road length, rural road length and total road length were used to allocate onroad emissions to a grid cell. In the United States, a project named “Vulcan” has quantified fossil fuel CO2 emissions for the contiguous U.S. at spatial scales of less than 10 10 km grid and temporal scales as fine as hours (Gurney et al., 2009a,b). The Vulcan inventory data for the year 2002 was constructed from
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seven emission data sets, which come in different forms (e.g., point or area form) and at different spatial scales (e.g., facility or parish level). Then onroad mobile emissions were downscaled from the parish level by allocating the hourly/parish/road/vehicle-specific CO2 onto roadways using a GIS road atlas with an underlying assumption that road traffic CO2 emissions are related to the road length and road class. There were important studies in South America (Chile specifically) aimed at obtaining traffic emissions. A spatial disaggregation approach was developed in mid-sized Chilean urban area using principal roads density (Osses de Elcker et al., 2008; Tuia et al., 2007), which gave the 1-km2 spatial distribution of hot emissions. Traffic count classification, land use, both combined with a simplified road network were regarded as surrogates or proxies in a large city (Santiago, the capital city of Chile) for deriving the 1-km2 spatial emissions (Saide et al., 2009). An inventory of air pollutant in Asia in the year 2000 was developed (Streets et al., 2003), in which a ‘‘top-down’’ approach was used to break regional- or country-based emissions data down to a variety of spatial resolutions from 1 1 to 30 s 30 s. Line sources emission allocation was conducted by multiplying pregenerated allocation factors by regional or national emission total. The allocation factors for line sources (road networks) were created by a combination and transformation of geographical information, such as road network, population distribution, and landcover classes. In the case of Europe, there was a three-tiered methodology for estimating emissions on EMEP (European Monitoring and Evaluation Programme) 50 50 km grid (European Environment Agency, 2009). First, national transport emissions may be allocated to road links based on measured or modeled traffic flow. Second, national transport emissions may be allocated using road network information and population based traffic intensity. Third, national transport emissions may be allocated also only by spatial surrogate (e.g. Population and land cover). Where appropriate traffic flow data are not available, emission distributions can be generated using digital road maps and population density data. For example, the APMoSPHERE project provided the emission estimates at 1 km resolution across the whole EU using a method, which was based on the assumption that population density was related to number of vehicles using different types of road network(highways, urban roads and rural roads). Then the road network was determined to be weighted according to the contribution to emissions in specific area (Briggs, 2005; Vienneau et al., 2009). These top-down disaggregation approaches to spatially allocate emission sources from a coarse geographic area to finer grid cells have proven to be reasonably accurate in emission estimates. The top-down approach usually can be roughly divided into two main steps: 1) allocation factors were supposed to correlate with emissions; 2) the coarse-scale estimates were allocated proportionally into fine-scale estimates. However, there are three shortcomings regarding the previous downscaling approaches. Firstly, the specific contribution of each allocation factor to total emissions was ignored. Only used was the ratio (percentage, proportion) of the amount of allocation factor in a grid cell to the total amount in a coarse geographic area. Grid cell emissions were calculated by multiplying the allocation factor’s ratio of grid cell by coarse-scale (e.g. parish) emissions. For example, in Vulcan project, the road segments were split by the 10 10 km grid cells, the grid cell emissions were calculated by multiplying the percentage of its original length by the original segment’s total emissions. The second shortcoming of most previous disaggregation approaches is that they have seldom incorporated population, income and urban area factors with road condition together in allocating emission estimates. Therefore, the emission estimates at finer scale were
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probably underestimated in high population density zone, urban center with high income. Likewise, those in low population density zone, like rural area, were probably overestimated. For example, only a simplified road network was used as an allocation factor for deriving spatial patterns of emissions in Chile (Tuia et al., 2007), which resulted in underestimation of emissions in urban centers, as well as overestimation in residential zones. Even though the APMoSPHERE project considered a few of allocation factors (population, land cover with road network) to broke these emissions down to 1 1 km grid cells, improvements also need to be made for modeling among emissions and these factors if the estimates are to be updated regularly. Obviously, it is necessary to build a multiple linear regression model to determine the relationship between allocation factors and traffic-related CO2 emissions. Currently, there is a remarkable tendency in the different countries towards the development of high-resolution emission estimates (1-km2) needed for policy-making on climate change and air pollution (Baldasano et al., 2008; Osses de Elcker et al., 2008; Saide et al., 2009; Tuia et al., 2007; Vienneau et al., 2009). And in order to overcome the two shortcomings of previous approaches, this paper proposed a new method based on a multiple linear regression model to perform spatial disaggregation of trafficrelated CO2 emissions from the parish-level scale to a 1-km2 grid scale. The State of Louisiana was chosen for the application. 2. Study area Situated in the south of the United States, the State of Louisiana was the 10th highest carbon dioxide emission state in the U.S. (U.S. Energy Information Administration (EIA), 2008a). The average percapita emission of Louisiana was approximately 40 tons of CO2 each year. CO2 emissions were inventoried by five sectors (U.S. Energy Information Administration (EIA), 2008b). The industrial sector accounted for 46 percent of all CO2 emissions from combustion of fossil fuels in 2005. The next two largest sources of carbon dioxide were the transportation sector (28 percent) and electric utilities (23 percent). The combined share of residential and commercial sectors was fairly small, approximately 3 percent (U.S. Energy Information Administration (EIA), 2008c). CO2 emissions data from the transportation sector for the year 2002 at the parish scale in Louisiana were obtained from the Vulcan inventory. The emission estimates contained three separate components: on-road emissions (mobile transport using designated roadways), non-road emissions (vessels along waterways, trains along railroads), and airport emissions associated with air travel (Gurney et al., 2009a). The CO2 emissions from road traffic took up 80.5% in Louisiana, and emissions from the other two components took up approximately 19.5%. There are 64 parishes in Louisiana, East Baton Rouge, Orleans, Jefferson, Caddo Parish are the four highest ones in CO2 emissions from road traffic. As background information for onroad emissions, according to the statistics in 2000 provided by the Louisiana Department of Transportation and Development (LDOTD), Louisiana had a total of 60,765 miles of road, 1,964,694 registered cars and 1,570,804 registered trucks. 3. Multiple linear regression model Regression analysis is widely used for prediction and forecasting. Multiple linear regression attempts to model the relationship between two or more explanatory variables and a response variable by fitting a linear equation to observed data. In this paper, we determined the correlation relationship between the allocation factors and CO2 emissions from road traffic with the average values per unit area (1-km2) at parish scale.
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Table 1 Data description and sources. Data
Form
Description
Coordinate system
Source
Date
CO2 emissions Income Parish Basic cell Road
Attribute Attribute Polygon Polygon Polyline
None None Geographic NAD83 Geographic NAD83 Geographic NAD83
Vulcan Inventory BEA of U. S. Census TIGER This project Census TIGER
2002 2002 2009 2010 2006
Urban area Population
Polygon Grid
On-road in each parish Income per person in each parish Boundaries of parishes 1 km 1 km Centerlines of road (Interstate, U.S, State and county highway, local, neighborhood and rural road, etc.) Based on the latest available governmental unit boundaries 30 s 30 s
Geographic NAD83 Geographic WGS 1984
Census TIGER LandScan
2006 2006
3.1. Allocation factors Selecting factors is the key to build the multiple linear regression model. Firstly, there have been a number of studies aimed at analyzing the factors that influence CO2 emissions. The IPAT model took the form of an equation combining environmental impact (I) with population size (P), affluence (A), and the level of environmentally damaging technology (T), known as I ¼ PAT. It’s still used for analyzing the driving forces of environmental change (York et al., 2002). The STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model is a stochastic model based on IPAT to estimate the effects of human population, affluence, technology on environment (York et al., 2003). CO2 emissions are produced when carbon-based fuels are burned. Energy consumption leads to CO2 emissions. Ordinarily, the more the population is, the more energy consumption there is. It is found that (Schafer and Victor, 1999) people increase their mobility, go longer distances when incomes rise. Thus, they use more energy and emit more CO2. It is believed that CO2 emissions are higher in the denser areas of towns (Reckien et al., 2007). There are three major factors that could significantly influence energy consumption, namely population growth, economic growth and high urbanization (Kazim, 2007; Liu, 2009). Secondly, in the transportation sector, the relationship between transport structure and CO2 emissions from road traffic has long been recognized (Newman and Kenworthy, 1989). Studies mainly
focused on transport infrastructure such as length and width of roads, speed allowance, supply of car parks/parking spaces and car ownership, as well as the availability, reliability, frequency of and distance to public transport. But such detailed traffic data at high resolution are very limited and difficult to obtain for a large study area (i.e. the State of Louisiana). Road length or road density have been used widely as a surrogate to allocate emissions in previous studies (Gurney et al., 2009b; Osses de Elcker et al., 2008; Saide et al., 2009; Streets et al., 2003; Tuia et al., 2007). Road density is computed by dividing the total road length within a geographic area by its area. Hence, we considered road density as an allocation factor to indicate transport structure. Therefore, population density, income, urban area and road density are regarded as allocation factors in this paper, which play different roles in explaining the amount of CO2 emissions from road traffic.
3.2. Data sources and data processing There are two groups of data. The first group is for building the multiple regression model between independent variables (population density, urban area, income and road density) and dependent variable (CO2 emissions) at parish scale. The second group is for disaggregating the CO2 emissions at 1-km2 grid cell. A number of spatial data or non-spatial data were required and were obtained for the project (see Table 1).
Fig. 1. The main steps involved in spatial data processing.
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Since only CO2 emissions and income value at parish scale were obtained respectively from the Vulcan inventory and the Bureau of Economic Analysis (BEA) of U.S. Department of Commerce, the other data in the first group should be processed in advance. The main spatial data processing is summarized in Fig. 1. It involves a number of GIS functions, including converting grids to points, intersecting, calculating geometric property, spatial join and summarization. These GIS functions are critical to this study. The procedures were carried out under the ArcGIS 9.2 platform. 3.2.1. Population density Firstly, the module of converting grids to points is needed to convert the LandScanTM Dataset developed by Oak Ridge National Laboratory (Bhaduri et al., 2007) into points. Each point indicates population number. Secondly, points are allocated directly to a parish within which they are contained by intersecting the points with parish boundaries. Thirdly, spatial join and summarization are used to give a summary of population of that points which fall inside it. Population density is computed by dividing population number by parish’s area. 3.2.2. Urban area An urbanized area percentage is used to reflect urbanization level. Firstly, a new dataset of new urban areas (polygons) contained within each parish is produced by intersecting the urban areas with parish boundaries. Secondly, the polygon’s areas are recalculated with geometrical attribute. Thirdly, spatial join and summarization are also used to give a summary of areas of that urban which fall inside it. The urbanized area percentage is computed by dividing the new urban area by parish’s area. 3.2.3. Road density Intersecting the roads with parish boundaries would create a new dataset of shorter roads length contained within each parish. The new roads lengths are computed again with calculating geometry function. Spatial join and summarization are used to give a summary of length of roads which fall inside it. Then road density is computed by dividing the total roads length by parish’s area. The second group data was obtained as the above way. All data in the first group has been divided by parish’s area, so we obtained the average values of per unit area (1-km2). And the unit is in accordance with that of the second group data. Although the regression model is built at the parish scale, the first group data to form the regression model is the value of unit area. So, in this sense, the scale can also be considered to be based on 1-km2. Therefore, it’s assumed to be reasonable and valid that we estimated CO2 emissions at 1 1 km grid cells with the second group data using a regression equation, which’s built with representative and average data sets of 64 parishes in the first group. 3.3. Regression analysis We attempted to model the relationship between the four independent variables and a dependent variable by fitting a linear Table 2 Correlations among CO2 emissions, population density, income, urban area and road density.
Pearson correlation CO2 emissions Population density Income Urban area Road density
CO2 emissions
Population density
Income
Urban area
Road density
1.000
0.956
0.575
0.903
0.835
0.956 0.575 0.903 0.835
1.000 0.512 0.815 0.766
0.512 1.000 0.583 0.589
0.815 0.583 1.000 0.765
0.766 0.589 0.765 1.000
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Table 3 Statistical performance indicators of different models.d Model
a
1 2 3 a b c d
R 0.956 0.980b 0.983c
R2
Adjusted R2
Std. error of the estimate
0.915 0.960 0.967
0.913 0.959 0.966
23.987 16.562 15.097
Predictors: (constant), Population density. Predictors: (constant), Population density, Urban area. Predictors: (constant), Population density, Urban area, Road density. Dependent Variable: CO2 emissions.
equation to observed data with multiple linear regression model, which is set as follows:
E ¼ b0 þ bp P þ bi I þ bu U þ br R
(1)
where the dependent variable E is total CO2 emissions (tons year1 km2); P is population density (persons km2); U is urbanization area (%); I is annual income per person (dallors km2); R is road density (km1). b0, bp, bi, bu, br are partial regression coefficients. We used a correlation and regression analysis of the first group data of the 64 parishes in the State of Louisiana (in section 3.2). Correlations between CO2 emissions and allocation factors can be found in Table 2. The analysis is based on the Pearson correlation coefficient. The correlation coefficients are all positive and significances of (one-tailed test) are zero, which suggests a significant correlation to the dependent variable (CO2 emissions). With the exception of income factor, all other factors have very high correlation with CO2 emissions. Since stepwise selection of the variables allows dropping or adding variables at the various steps in either direction, it couldn’t happen that any significant variables are dropped or non-significant variables are added in model. Therefore, a stepwise selection method was chosen, which reiterates the analysis by each parameter in turn and independently considers the inclusion or exclusion of the parameters with every step (criteria: probability-of-F-toenter <¼ 0.05, probability-of-F-to-remove >¼ 0.1). Although Eq. (1) includes all the allocation factors, not all of these factors may be statistically significant. The income factor with the largest probability of F is removed. Model 1 is the simplest equation including only population density variable. Then the impact from urban area is added in Model 2, which explanation capacity is improved (R2 ¼ 0.960 > 0.915). The explanation capacity of Model 3 is improved slightly (R2 ¼ 0.967), but the standard error of the estimate is reduced (SEE ¼ 15. 097 < 16.562) when the impact of road density is added. By comparison with other models preformed, Model 3 (including population density, urban area, and road density) should be regarded as the best one, which means that the cumulative effect of the three factors on CO2 emissions is significant (see Table 3). The regression model (Eq. (2)) shows that the associated F-Test statistics for overall is 590.841 and significance is 0.000. T-test significances of all three allocation factors are less than 0.05 (see Table 4). It can Table 4 Coefficients of model. Model 3
(Constant) Population density Urban area Road density Total model
Unstandardized coefficients
Standardized coefficients
B
Std. error
Beta
2.165 0.547 312.502 16.439
5.483 0.040 44.280 4.487
0.596 0.307 0.144
T or F
Sig.
0.395 13.664 7.057 3.664
0.694 0.000 0.000 0.001
590.841
0.00
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Fig. 2. Spatial disaggregation of traffic-related CO2 emissions from parish scale to 1 1 km grids in the U.S. State of Louisiana.
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significantly impact a prediction in CO2 emissions from road traffic when population density, urban area, road density are taken into account. The following is the regression model that we obtained for the given data:
E ¼ 2:165 þ 0:547P þ 312:502U þ 16:439R
(2)
4. Results and discussions We used the estimated coefficients of the multiple linear regression model (Eq. (2)) to project CO2 emissions from road traffic at 1 1 km gird for the year 2002. Thus, Total amount of emissions at parish level had been disaggregated with the second group data of Louisiana (in section 3.2). The current method has involved 135,164 1 1 km grid cells for the State of Louisiana. The resolution is sufficient to provide information regarding CO2 emission patterns on a finer scale. Fig. 2 shows the main steps of spatial disaggregation of trafficrelated CO2 emissions from parish scale to 1-km 2 grid cells. At the parish level, the four highest CO2 emission parishes in Louisiana are East Baton Rouge, Orleans, Jefferson and Caddo Parish, which had emission values higher than 350,000 tons per year in 2002 (Fig. 2 (a)). Thereby the four parishes were chosen to show distribution pattern of CO2 emissions at 1-km2 gird cells after spatial disaggregation (Fig. 2(c)). In the four highest parishes, the mean gird value is 219 tons. There are 505 grids that are above the mean grid CO2 emission in East Baton Rouge, 202 grids in Orleans, 296 girds in Jefferson, 330 grids in Caddo. There are 167 grids that have no traffic-related emissions in East Baton Rouge, 512 grids in Orleans, 1209 grids in Jefferson, 357 grids in Caddo. The highest grid value is 6929 tons in Orleans parish, 5630 tons in East Baton Rouge, 4785 tons in Jefferson, 2904 tons in Caddo. We could conclude that East Baton Rouge has the biggest area in which emitted traffic-related CO2 emissions exceeds the average. It also has the smallest area with no traffic-related emissions. It is Orleans that has the most CO2 emissions per 1-km2. Comparing the original traffic-related CO2 emissions by parish with the 1-km2 grid estimates, the gridded map shows variation within a parish, which allows the identification of the emission “hot spots”. There are some hot spots identified in the gridded map (Fig. 2 (c)), Baton Rouge in East Baton Rouge Parish, Shreveport in Caddo Parish, New Orleans in Orleans parish, Gretna, Harvey, Kenner, Marrero, Metairie, Terrytown and so on in Jefferson Parish. Other parishes of interest can also be disaggregated spatially in the same way. The method proposed in this paper is based on the assumption that population density, road density, urban area, income have effects on traffic-related CO2 emissions. As the highest CO2 emission parish, East Baton Rouge was selected to present spatial distribution of selected allocation factors at 1-km2 grid cells (Fig. 2 (b)). The result (Fig. 2(b) and (c)) reveals that high CO2 emissions are concentrated in dense road network of urban areas with high population density and low CO2 emissions are distributed in rural areas with low population density, sparse road network. Compared with the previous studies at 1-km2 grid scale (Tuia et al., 2007), it could minimize underestimation in the main urban area, high loaded road network, or in high population density area. Also it could minimize overestimation of emissions in rural region. A multiple linear regression model in this paper was built to determine specific contribution of each allocation factor to total emissions, so that it’s less time-consuming to calculate or update emission estimates regularly than those methods without regression models (Briggs, 2005; Vienneau et al., 2009). In the light of the standardized beta values for all three factors, it tells us that population has greatest impact on CO2 emissions, and
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road density has least impact in Model 3 (see Table 4). The most important limitation of the method in this paper is that only road density is determined to represent the traffic network for neglecting differences in traffic flow and speed of vehicles. Thereby, the method is not able to simulate the real road traffic situation. But we extremely recommend that a spatial redistribution that takes into account detailed traffic data would be worth being carried out in hot spot once it’s identified by the method proposed in this paper. We also acknowledge that there is still uncertainty in the emission estimates at grid cell level because of the error or gaps in the original data. The spatial uncertainty in this method probably resulted from spatial data process too. For an example, minor errors were expected during the conversion from population grids to points and overlay analysis between parish boundaries or grid cells and population points. Because estimates accuracy is affected by the model, we would monitor CO2 emissions at different sites or representative sample of locations within the state to evaluate and improve our model for estimates accuracy in the future research. 5. Conclusions Basically, we applied a top-down approach based on a multiple linear regression model to break parish-based emission data down to 1-km2 gird cells, using the State of Louisiana as an example. Considering the allocation factors (population density, urban area, income, and road density) together, we used a correlation and regression analysis to determine the relationship between these factors and traffic-related CO2 emissions. The best-fit model was developed using stepwise selection method, which included population density, urban area, and road density, and excluded income factor because of the largest probability of F. We built the model with the average values per unit area (1-km2) at parish scale and employed it to calculate the emissions at 1 1 km grid cells in the study area. Therefore, traffic-related CO2 emissions can be faithfully allocated “down” to each grid cell. The resultant grid estimates derived from the proposed method hence are considered more accurate and less time-consuming than the previous methods. At the same time, the method is very flexible such that different assumptions or different factors can be incorporated in future studies. For example, more factors could be taken into account in improving the multiple linear regression model, such as the road type, road lanes, and traffic volumes and so on. The data will serve as a critical input to many energy or climate impact modeling efforts and provide a baseline dataset for policy development and decision making. References Baldasano, J.M., Guereca, L.P., Lopez, E., Gasso, S., Jimenez-Guerrero, P., 2008. Development of a high-resolution (1 km 1 km, 1 h) emission model for Spain: the high-elective resolution modelling emission system (HERMES). Atmospheric Environment 42 (31), 7215e7233. Bhaduri, B., Bright, E., Coleman, P., Urban, M., 2007. LandScan USA: a high resolution geospatial and temporal modeling approach for population distribution and dynamics. GeoJournal 69, 103e117. Briggs, D., 2005. APMoSPHERE (Air Pollution Modelling for Support to Policy on Health, Environment and Risk Management in Europe). Imperial College of Science, Technology and Medicine, London. European Environment Agency, 2009. EMEP/EEA air pollutant emission inventory guidebook-2009. EEA Technical Report. Gurney, K.R., Mendoza, D., Geethakumar, S., Zhou, Y., Miller, C., Sahni, N., Seib, B., Ansley, W., Fischer, M., Du Can, S.D., 2009a. Vulcan Science Methods Documentation, Version 1.1. Gurney, K.R., Mendoza, D.L., Zhou, Y.Y., Fischer, M.L., Miller, C.C., Geethakumar, S., Du Can, S.D., 2009b. High resolution fossil fuel combustion CO2 emission fluxes for the United States. Environmental Science & Technology 43 (14), 5535e5541. Kazim, A.M., 2007. Assessments of primary energy consumption and its environmental consequences in the United Arab Emirates. Renewable & Sustainable Energy Reviews 11 (3), 426e446.
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