Due to constantly rising air pollution levels as well as an increasing awareness of the hazardousness of air pollutants, new laws and rules have recently been passed. Although there has been a large amount of research on this topic, bibliometric data is still to be collected. Thus this study provides a scientometric approach to the material published on this subject so far.For this purpose, data retrieved from the "Web of Science" provided by the Thomson Scientific Institute was analyzed and visualized both with density-equalizing methods and classic data-processing methods such as tables and charts.For the time span between 1955 and 2006, 26,253 items were listed and related to the topic of air pollution, published by 124 countries in 24 different languages. General citation activity has been constantly increasing since the beginning of the examined period. However, beginning with the year 1991, citation levels have been rising exponentially each year, reaching 39,220 citations in the year 2006. The United States, the UK and Germany were the three most productive countries in the area, with English and German ranked first and second in publishing languages, followed by French. An article published by Dockery, Pope, Xu et al. was both the most cited in total numbers and in average citation rate. J. Schwartz was able to claim the highest total number of citations on his publications, while D.W. Dockery has the highest citation rate per publication. As to the subject areas the items are assigned with, the most item were published in Environmental Sciences, followed by Meteorology & Atmospheric Sciences and Public, Environmental & Occupational Health. Nine out of the ten publishing journals with more than 300 entries dealt with environmental interests and one dealt with epidemiology.Using the method of density-equalizing mapping and further common data processing procedures, it can be concluded that scientific work concerning air pollution and related topics enjoys unbrokenly growing scientific interest. This can be observed both in publication numbers and in citation activity. Air pollution is defined as the emission of particulate toxic elements into the atmosphere by natural or anthropogenic sources . These sThe World Health Organization (WHO) estimates 2.4 million fatalities due to air pollution each year ,6, most Despite such enormous scientific and legislative efforts to measure and improve air quality levels, many people are still exposed to hazardously polluted breathing air on a daily basis ,10. FurtThe present scientometric compilation and analysis is intended to identify current scientific efforts with regard to air pollution, as well as to highlight research gaps requiring further attention.All analyzed data were retrieved from Thomson Scientific's online-database "Web of Science". To provide a comparison data was also collected in some cases from "PubMed", the online database of the U.S. National Library of Medicine.For the query, the terms "air pollution" and "air quality" were connected with the Boolean operator "OR" and entered in the search field "General Search".The time frame was limited to the period between 1955 and 2006. For this purpose, the "Change Limits and Settings" function was adjusted initially and the query was made under this presetting. The limitation was performed since the years 2007-2009 were not finalized in the databases completely.Results were also analyzed by means of citation. Therefore, the feature "Citation Report" was used to calculate the citation rate of both authors and citations per year of citation. To calculate the citation rates of the singular authors, results were first sorted by author. Afterwards, the ten most productive authors' publications were put under citation analysis. The average citation rate is the quotient of the total citation number divided by the publications listed for the author in question.The "Web of Science" database provides several tools to analyze entries according to specified parameters . The datSoftware using the method of density-equalizing mapping was employed to determine international correlations. This method resizes countries proportionally according to a predefined variable. In this study, the territory with the highest number of publications is depicted largest on the associated map. The basic principle was developed by Gastner and Newman .The overall number of items listed in the database served as a measure of both public interest and scientific productivity concerning the topic of air pollution. The comparison of results in "Web of Science"  and "PubMed"  indicated some differences. Entries in the "Web of Science" displayed a comparatively constant number over 25 years 1966-1990), following lower numbers in the prior decade (1955-1965). "PubMed" results, however, differed: Beginning with the year 1957 publication numbers increased up to a preliminary maximum in 1971. Subsequently, decreasing publication numbers equalized that of the "Web of Science" results again in 1978. After a period of relatively constant publication quantities (1978-1990), both databases showed an exponential increase in yearly published items. Despite recent points of nominal decline  the upward tendency remained consistent through 2006 , the numbers show a development similar to publication data. However, the increase since 1991 is marginally sharper than that of the publication numbers fig. .Usage of the analysis tool "Countries/Territories" indicates that the USA holds the most publications by far. The UK and Germany ranked a distant second and third. The ten most publishing countries produced 76.44% of all the entries in the inquiry time frame. fig.  Density-As to the analysis by language, results showed a division consistent with the results obtained in the "country" analysis. The percentage of items published in English (96%) was much higher than the fraction published by Anglophone countries fig. .To obtain the average citation rate of single countries, the total number of citations for publications originated in each country was divided by the number of the said publications. In conclusion, Botswana achieved the highest rate with 191 citation/item, followed by Malta with 153.2/item. No other country achieved a citation rate higher than 30/item fig. .Inclusion of a threshold of at least 30 publication and density-equalizing calculations leads to a cartogram shown in figure The average citation rate was calculated both for the most productive authors and the most cited publications. While the single author's citations had to be divided by his number of publications, the publications' total number of citation was divided by the number of years of citation activity for this item ("citations per year").As for the publications' total citations, the most cited article was written by Dockery, DW, Pope, CA, Xu, XP et al. and published in the New England Journal of Medicine in 1993. The ten most cited entries in terms of total citation are shown in Table Examining the ten most productive authors' publications, J. Schwartz was cited most often in terms of total numbers fig. , while DAnalysis of the subject areas assigned to the publications revealed only two involved medical issues in the first ten areas. The other eight areas dealt either with environmental questions or with matters related to engineering fig. .The most commonly published document type was the article, followed by the meeting abstract. The ten most frequently used document types made up for 99.77% of all publications  than the one in "Web of Science", the entries were concerned more with environmental and technical interests than with medical subject matters. The first journal solely dedicated to toxicology  was ranked thirteenth.Air pollution has always been a subject of public concern. Particularly from the mid-twentieth century onwards, there has been a growing societal impetus to curtail and counteract the hazardous effects of air pollution. In recent decades, as anthropogenic air pollution has reached historically high levels, international public and scientific interest has intensified towards this topic. While natural, stationary emission cannot be significantly influenced, a major focus has been given to changes in man-made pollutant emissions. Heretofore, there has been no comprehensive analysis of data available on this topic. The present study sought to provide bibliometric data on research activity related to the subject air pollution, analyzed and displayed with both common and innovative data processing methods such as density-equalizing calculation. The examined time frame was limited to the period after 1955, since global research activity was relatively low before that date. The year of 2007 was excluded from the analysis because of incomplete data.Mirroring public and scientific interest, general research activity on the topic of air pollution has increased steadily since the beginning of the analyzed period. However, a remarkable boost in both publication and citation activity can be observed at the beginning of the 1990s. On the one hand, this can be attributed to the availability of public internet access . Before Regarding the origin of publications, US researchers contribute by far the largest amount of scientific output related to air pollution. The second ranking of Great Britain, the third ranking of Germany and especially the People's Republic of China in fourth position is notable as well. Mega-Cities are defined as urban agglomeration areas with more than 10,000,000 inhabitants. They are characterized by social challenges such as poverty and crime but above all for producing massive environmental burdens especially in the field of air quality . ConsideEnglish appears as the most common language in scientific releases; a finding that is concordant with papers' distribution to countries. While German plays a comparatively major role too, Chinese cannot be found among the most frequently used languages. This disproportion might lead to the assumption that English is used more commonly among Chinese researchers compared to European scientific publications. While French ranked third among the publications' languages, France's number of publications ranked seventh - past Italy and China whose languages do not appear at all among the most common ones. Russian also appears among the most frequently used languages, though Russia's publications ranked 27th in the international list. These facts could either be ascribed to political conditions and convictions, or to closer or broader affinity of languages. It is possible that states belonging to the former USSR still use Russian as their scientific language after the independency from the Russian government. It is also possible that Baltic and Eastern European States tend to rely more on research by their former authority (i.e. Russia) than Western European or American researchers and therefore use rather Russian than English in publications. Additionally, Slavic languages are linguistically closer related to Russian than to English. Older researchers might also have difficulties writing in a new language after using Russian for several decades.While the total amount of publications was used as a distinguishing mark for research quantity, the average citation rate was introduced to evaluate the single nations' research quality . In thisAmong the most cited articles, the aforementioned international asthma study is itself exceeded by an article by Dockery et al. in the New England Journal of Medicine, on the association between air pollution and mortality in six US cities . This isAs an author, D.W. Dockery features the highest average citation rate (125.54). However, in total citations, Dockery is only second to J. Schwartz.With regard to "subject areas", the environmental sciences have produced the most results so far on the topic of air pollution, followed by the atmospheric sciences. Noticing that among the ten leading research fields there are only three concerned with health aspects, it can be said there is a rather significant lack of medical research on this subject. Given an estimated 2.4 million deaths yearly due to air pollution, it is rather surprising that medical research has lagged by this degree. Considering all the severe consequences polluted air has on public health, on international health conditions, and health care costs, it is justified to point out this obvious research gap, and recommend further scientific efforts in medicine in the future.Since most of the publications are articles, it can be said that most of the scientific endeavours have been embossed by initiatives rather than by efforts in analysis and description of statistical coherences.Among the ten most publishing journals, nine are occupied with environmental issues and one with epidemiology. This again demonstrates the existing deficit in medical interest.However, it may be that medical interest in the field of air pollution and its consequences for human health may have developed only recently. Despite the fact that polluted air has always been a major threat on human health, there has not been any major supra-regional effort to change the polluting behaviour in the past 20 years. Although people have been suffering from polluted air for a along time, beginning several centuries ago, there has been no substantiated evidence for the connection between polluted air and deaths due to pulmonary diseases. New conclusions about coherences between certain air pollutants and their impact on public health have only been drawn recently, and therefore it is entirely possible that the medical research efforts will eventually catch up to and even exceed the scientific work already done in environmental fields.Hereby given the first comprehensive analysis of scientometric data on the subject of air pollution, it can be said that scientific interest in this topic has steadily increased to the present day. However, there is to be noted a major research gap in terms of medical analysis. The major contingent of data originates from the USA and an even larger amount is written in the English language. Considering research quality as measured in terms of average citation, less productive countries such as the Netherlands and Sweden show the best results. A generally larger effort towards medical research into air pollution is strongly indicated at this time.The authors declare that they have no competing interests.HZ carried out the bibliometric investigations, participated in analyzing results and drafted the manuscript. CS participated in data research and performed the scientometric analysis. DQ, NS, SM participated in the design of the study. KV and SU carried out pilot data research. DAG participated in its design and data analyses. MS conceived of the study, and participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.


A community health assessment (CHA) is used to identify and address health issues in a given population. Effective CHA requires timely and comprehensive information from a wide variety of sources, such as: socio-economic data, disease surveillance, healthcare utilization, environmental data, and health resource allocation.Indonesia is a developing country with 235 million inhabitants over 13,000 islands. There are significant barriers to conducting CHA in developing countries like Indonesia, such as the high cost of computing resources and the lack of computing skills necessary to support such an assessment.At the University of Pittsburgh, we have developed the Spatial OLAP  Visualization and Analysis Tool (SOVAT) for performing CHA. SOVAT combines Geographic Information System (GIS) technology along with an advanced multidimensional data warehouse structure to facilitate analysis of large, disparate health, environmental, population, and spatial data.The objective of this paper is to demonstrate the potential of SOVAT for facilitating CHA among developing countries by using health, population, healthcare resources, and spatial data from Indonesia for use in two CHA cases studies.Bureau of Statistics administered data sets from the Indonesian Census, and the Indonesian village statistics, were used in the case studies. The data consisted of: healthcare resources , population (census), morbidity and mortality, and spatial (GIS-formatted) information.The data was formatted, combined, and populated into SOVAT for CHA use. Case study 1 involves the distribution of healthcare professionals in Indonesia, while case study 2 involves malaria mortality. Screen shots are shown for both cases. The results for the CHA were retrieved in seconds and presented through the geospatial and numerical SOVAT interface.The case studies show the potential of spatial and multidimensional analysis using SOVAT for community health assessment in developing countries. Since SOVAT is based primarily on open-source components and can be deployed using small personal computers, it is cost-effective for developing countries. Also, combining the strength in analysis and the ease of use makes tools like SOVAT ideal for healthcare professionals without extensive computer skills. Effective community health assessment (CHA) requires timely and comprehensive information from a wide variety of sources . A CHA mIndonesia, with its 235 million inhabitants and more than 13,000 islands, is the world's 4th largest country (in terms of population). The use of a spatial decision-support system for CHA can greatly enhance the health assessment process. Until recently, most of the visible CHAs in Indonesia were conducted by international organizations. For example:• An environmental assessment after the Aceh tsunami (December 2004) to identify the environmental and community issues related to the disaster and to prioritize reconstruction processes .• An assessment of the poverty and damage to health facilities after the Yogyakarta earthquake May 2006) to provide a reconstruction plan for the victims [ to proviThese examples suggest that the challenges in integrating and analyzing data from various sources require skills and resources that can only be assembled in the face of catastrophic events and by international organizations. The availability of a visual decision-support system can simplify the process of data integration and analysis and make the CHA process affordable to local and regional government offices in Indonesia.The results presented in this paper are case studies of CHA in Indonesia by combining numerical and spatial data sets. The source of the numerical data is primarily from the Village Potential Statistics (PODES) of 2003, while the source of the spatial data is from the Indonesian Bureau of Statistics. There were a total of 60,000 villages in Indonesia in 2003. Data are collected from each individual village and then aggregated into a sub-district level. We integrated the numerical data with spatial data consisting of three levels of administrative boundaries: provinces, counties, and sub-districts. Administrative divisions in Indonesia consist of four levels. The country is divided into 33 provinces, and each province is sudivided into counties/regencies/city, which are further subdivided into subdistricts (kecamatan), and again subdivided into villages. Since subdistrict is usually the lowest unit for planning and assessment, we use the first three administrative levels. The result of the integration is a multidimensional database of 33 provinces, 445 counties, and 4,000 sub-districts.The village statistics data sets contain indicators that are related to environmental and health conditions. The health-related variables include disease outbreak and mortality number, primary water sources, waste-treatment facilities, number of health facilities and medical staffs, natural disaster, and pollution.There are two study cases presented in this paper. The first one is the distribution of physicians in Indonesia. The second one is the comparison of mortality numbers due to malaria diseases in rural and urban areas.Indonesia has an uneven distribution of population among its major islands. More than two-thirds of the country's population of 235 million is squeezed into the small Java island, just over 7% of its land mass. By contrast, the island of Papua in the easternmost part of the country represents 22% of the total land mass, yet has only 1% of the population. Like most countries, healthcare facilities and healthcare professionals tend to be concentrated in urban areas. Multidimensional analysis can provide information as to whether rural areas are indeed underserved by healthcare professionals and facilities. It can also identify which rural areas are in greatest need of healthcare services. The analysis of CHA can potentially be useful for planning and resource allocation. In this case study, healthcare service is represented by the number of physicians in the region.To analyze the number of healthcare professionals, two measurements have been constructed in SOVAT: the number of healthcare professionals and the ratio of healthcare professionals/100,000 population. Healthcare professionals consist of physicians, nurses, and midwives. A dimension called healthcare professional is constructed in SOVAT with physician, nurse, and mid-wife as its members. Using this dimension, researchers can select which healthcare professionals he or she wants to analyze by choosing one of the members of the dimension.Figure drill-out from the pop-up menu. The result is shown as in Figure The analytical feature of SOVAT provides users with such functions as sorting the chart in ascending or descending order, and viewing only the top 5 or 10 results. Users can also focus on certain areas of the map to get more detailed information about the area. For example, suppose the user is interested in the province of South Sumatra and wants to compare it to neighboring provinces. To do that, the user can just select the South Sumatra province from the map and click drill-down. Figure If the user wants to focus on all counties in one province, s/he can select the province and go to Figures The mortality rate due to malaria is usually higher in rural areas. Using the multidimensional analytical feature in SOVAT, users can determine if this is the case with Indonesia. To do this analysis, "Mortality Number" is used as a measurement in conjunction with a dimension called "Diseases." Malaria is one of the diseases being tracked in the "Disease" dimension. In addition to the "Mortality Number" measurement and "Diseases" dimension, we also use another dimension called "Area", whose members comprise "Urban" and "Rural." By analyzing the data using two dimensions , we can compare the mortality number of malaria in urban and rural areas.Figure The result, as shown in Figure This kind of analysis allows public health professionals to determine which areas need priority in malaria prevention. Our results conclude that the eastern part of Indonesia, especially the rural areas, is in greatest need.The case studies show the potential of spatial and multidimensional analysis using SOVAT for CHA in developing countries.In CHA, timely information is important for decision making. The process of data integration and analyses has been the stumbling block in the CHA process. Using an integrated tool that makes the process fast and easy will allow the assessment to be conducted rapidly. A faster CHA process, in turn, will provide timely information to support the decision-making process by public health professionals.The strength of multidimensional and spatial analysis is that it allows the user to see the information visually on a map that is otherwise hidden in the complexity of the data and variables. Since the information is presented visually and no statistical skills are required, this type of decision-support system can potentially be used by more users and higher level executives.Spatial and multidimensional analysis can be even more useful when extensive data from various sources are available. The case studies presented in this paper can be extended by adding more data sets, for example disease surveillance data, hospitalization data, or environmental data. The more data sets integrated into the decision-support system, the more dimensions and richer information that can potentially be uncovered by the system, hence providing higher quality of information for public health decision makers.SOVAT is an integrated decision support system, and is not designed to replace GIS systems such as ArcGIS. The map capabilities in SOVAT include basic operations such as zoom in/out and buffering, handling of vector data such as point and line, exporting to an image, and printing the map. However, the map in the current version of SOVAT cannot be used to handle more advanced GIS operations such as map projection and map customization.SOVAT  is a novel decision-support system developed by the University of Pittsburgh . SOVAT cThe architecture of SOVAT is shown in Figure Through an easy-to-use point-and-click interface, even a novice user is able to conduct complex queries quickly and effortlessly using SOVAT. SOVAT DSS has been designed to present health information in a way that will facilitate planning efforts among community stakeholders with diverse interests. The two main components that make up SOVAT interface are the navigation and visualization component , as dispThe Indonesian data sets are comprised of demographic data, health indicators, and spatial data (maps). The data sets come in different levels of detail and were collected using different collection methods. Indonesia is divided into provinces. Provinces consist of regencies (kabupaten) and cities (kota) which together are called counties in this paper. One level below counties is sub-district (kecamatan), while the lowest administrative level – that is the one below sub-district – is called village (desa). The definition of village applies to both rural and urban areas. Political changes in Indonesia since the last decade have affected the administrative division, with the tendency of a growing number of provinces and regencies. The latest data from the Ministry of Internal Affairs show that Indonesia currently has 33 provinces and 445 counties . There aData sets from the Indonesian Census and the Indonesian village statistics were used in the case studies. Similar to many countries, a census in Indonesia is conducted every decade. Indonesia conducts a series of population, agricultural, and economic censuses. The population census takes place in the years ending with "0"; the agricultural is conducted in the years ending with "3"; while the economic census is held in the years ending in "6" . Of thes• Providing information of potential and actual development in the village by providing socio-economic conditions and available facilities;• Providing a database for regional planning as well as a progress report on the development at the village-level; and,• Providing core data of the small area statistics.The information in village-level collection includes: the number of the population and households, the housing and environmental data, the education and health-related data, socio-cultural information, recreation and sport facilities, transportation, and communication.While demographic data are mostly from the Bureau of Statistics (BPS), more specific health indicators are available from the Ministry of Health . These dSOVAT uses spatial data in polygon format that consists of administrative-boundary maps. The map is rendered using certain color schemes to display the results of OLAP queries. For example, in Figure The digital map of Indonesia is provided by the BPS. The existing spatial data come from four different levels: from province level down to village level. However, due to the low accuracy of the village-level spatial data, we chose to use one level higher than the village – that is the sub-district (kecamatan) level.Geographic location was used as the primary linkage variable that connects statistical and spatial data sets. The linking process is done using an administrative code that is uniquely defined for every administrative unit. Standardization is the key for the linking process. Most developed countries have a uniform identification for every geographic entity that can be used by the government and private sector. For example, in the United States there is the Federal Information Processing Standards codes (FIPS codes), a standardized code for every geographic entity in the US issued by the National Institute of Standards and Technology (NIST). This code is used by the US Census Bureau and other government agencies that generate statistical data sets.Unfortunately, there is no such uniform identification standard for geographic entities in Indonesia. The lack of a uniform code leads to the use of geographic names (such as the name of counties and villages) as the key identifiers of the geographic entity. Geographic names are very susceptible to typographical errors and inconsistent spelling. As a result, the same geographic entities can be written differently in different reports even if the reports come from the same government institution (Bureau of Statistics). Several solutions were tried for this problem. Some of the data were corrected using a pattern-matching approach, while the remainder that could not be recognized using pattern matching were manually corrected.The need of a uniform code is more important in light of the rapid changes of administrative boundaries in the past decade. The problem with spatial data becomes more complex since the updating process of spatial data is not as fast as the process of administrative changing. While the administrative code is easily updated with more recent changes, the map is still outdated. For example, the administrative boundaries are changed up to 2007, however the latest version of the map we use is from 2000.Since there is no official new map released, there is no other option rather than to translate the data into an older map.As shown in Figure The authors declare that they have no competing interests.BP and MVP conceived the system, conducted the analyses, and wrote the final version of the manuscript. WS and MVP conducted data integrations. GP wrote the program for and conducted data cleansing. MS wrote the abstract and involved in the development of SOVAT, DSB lead the MIDAS Indonesian project that initiated the collection of Indonesian health statistics.


Two research strategies may reduce health disparities: community participation and the use of geographic information systems. When combined with community participation, geographic information systems approaches, such as the creation of disease maps that connect disease rates with community context, can catalyze action to reduce health disparities. However, current approaches to disease mapping often focus on the display of disease rates for political or administrative units. This type of map does not provide enough information on the local rates of cancer to engage community participation in addressing disparities.We collaborated with researchers and cancer prevention and control practitioners and used adaptive spatial filtering to create maps that show continuous surface representations of the proportion of all colorectal cancer cases diagnosed in the late stage. We also created maps that show the incidence of colorectal cancer.Our maps show distinct patterns of cancer and its relationship to community context. The maps are available to the public on the Internet and through the activities of Iowa Consortium for Comprehensive Cancer Control partners.Community-participatory approaches to research are becoming more common, as are the availability of geocoded data and the use of geographic information systems to map disease. If researchers and practitioners are to engage communities in exploring cancer rates, maps should be made that accurately represent and contextualize cancer in such a way as to be useful to people familiar with the characteristics of their local areas. Cancer health disparities, defined as "differences in cancer incidence (new cases), cancer prevalence , cancer death , cancer survivorship, and burden of cancer or related health conditions that exist among specific population groups" , are a pMaterials that guide comprehensive cancer control activities do not address spatial variation in cancer prevalence . This abTwo research strategies have been employed to understand and address disparities. First, as evidence of disparities has grown, some have called for a change in focus from uncovering variation to reducing disparities . There iSecond, researchers are making more use of geographic information systems (GIS). GIS can uncover spatial variation in environmental exposures, social phenomena, and health outcomes, which will enable interventions to be targeted spatially. In addition, geocoded data are increasingly available, which makes GIS investigations increasingly possible. Applications of GIS in health include disease mapping, spatial epidemiology, and support for spatial decisions regarding provision of health care -15. GIS Current approaches to disease mapping often make use of political or administrative borders, such as counties, to display disease rates. This type of mapping is problematic. It masks variation within units of area, gives the impression that a rate is static throughout the unit chosen, and often displays unstable disease rates in sparsely populated areas. A reliable spatial basis of support is needed to calculate and spatially represent disease rates ,18.We initiated a pilot mapping project to 1) build a collaboration between university researchers and cancer prevention and control practitioners and 2) open a dialogue about spatial patterns of cancer with the public by creating and distributing maps that accurately represent and contextualize the cancer burden.This project began with a collaboration between University of Iowa researchers and the Iowa Consortium for Comprehensive Cancer Control (ICCCC), which comprises stakeholders from various backgrounds, including researchers, legislators, and cancer survivors. The ICCCC works to develop and implement a comprehensive cancer control plan for Iowa . One of We proposed to create maps that represent rates of cancer as a continuously varying surface, as opposed to one divided by political or administrative boundaries. We would incorporate into those maps cues to the local context, enhancing the relevance to local communities. The maps would be distributed to the public via the World Wide Web and the activities of ICCCC. On the basis of the University of Iowa Institutional Review Board's recommendation, we withdrew our application for review because no human subjects were at risk.We obtained cancer incidence data from the Iowa Cancer Registry through a data-sharing agreement. We received no individual identifiers with the data.We obtained population data for Iowa zip code tabulation areas (ZCTAs) from the State Data Center of Iowa. The US Census Bureau discontinued tabulation of population information by zip code and began tabulating by ZCTA with the 2000 census. ZCTAs are generalized areal representations of zip codes and are built from census blocks. In calculating cancer incidence, population data for the ZCTA was used with cancer data for the zip code; thus, our incidence measurement is subject to spatial misalignment between zip code and ZCTA. This problem is partially controlled by aggregating the zip codes within the spatial filter areas (www.uiowa.edu/~gishlth/DMAP4/). To illustrate the steps taken and the results of our work, we focus on maps of colorectal cancer incidence and late-stage colorectal cancer diagnosis. It is widely argued that "tracking the rates of distant, or late, cancers is a good way to monitor the impact of cancer screening. When more cancers are detected in the early stages, fewer should be detected in the late stages" , Microsoft Excel spreadsheet software , ESRI ArcMap software , and DMAP IV software . For each zip code, the expected number of colorectal cancer cases (or the number of late-stage diagnosis cases) of each cancer was computed by applying the statewide rates to the numbers of people (or cases) in each age-sex group in the zip code, for incidence (or proportion of late-stage diagnosis cases).We mapped the ratio of observed cases to expected cases, showing how the rate of cancer incidence and late-stage detection varies across the state. Unfortunately, because of small numbers, we could not calculate rates adjusted by race or ethnicity, although disparities in cancer rates may exist for these groups and may warrant further investigation.We created the maps by using adaptive spatial filtering ,23 that To implement spatial filtering using aggregated data, we had to place our cancer data for each zip code at a single point within the zip code area. A common choice made by researchers is to use the centroid of an area, which is problematic for 2 reasons. First, a true centroid of a zip code cannot be determined because boundaries for zip code areas are not known. Second, using the centroid is not ideal because it is a representation of the center of land area in a zip code — not the center of population — and, thus, sometimes falls in an unpopulated location, influencing the spatial patterning of estimated rates. For each zip code, we selected the point location of the populated place that falls within the largest incorporated area in the corresponding zip code to represent the population. We obtained shapefiles of populated places and incorporated areas from a state geospatial data library . Where nWe joined the observed and expected numbers for each zip code to this set of point locations and calculated indirectly standardized rates for each grid point on a 3-mile grid. The size of the grid is relevant only to the degree that it is much finer than the geocode used for the data of interest — in this case, the zip code center. Our adaptive filter, centered on each grid point, expanded until it included at least 50 expected cancer cases as aggregated to the zip code level. We then interpolated the area in between grid points by using inverse distance weighting and the 8 nearest neighbors to create a continuous surface.To make our maps meaningful in terms of local cancer rates, we made the following choices in producing our final maps. We overlaid our cancer burden layer at 50% transparency with a 1:100,000-scale US Geological Survey topographic map of Iowa, to allow topographic and other settlement information to be seen through the cancer layer. We also included geographically positioned names of major cities and highways to enhance the sense of place. For each map, we used a color scheme that transitioned from red to blue to indicate "hot" and "cold" spots — those that experienced rates higher and lower than the statewide rate, respectively. The 15 classes were determined by using quantile breaks; we used a large number of classes to present a smooth transition between categories and not give the impression of sharp changes in rates. With each map, we included a short description of the method used in its creation.r = −0.0688). Some places in the state experience high or low rates of both incidence and late-stage diagnosis. The area around Storm Lake in northwest Iowa, for instance, experiences elevated rates of both incidence and late-stage colorectal cancer. These elevated rates could be related to the fact that this community has a high percentage of minority populations, in particular the Tai Dam/Lao and Hispanic communities; 21.1% of Storm Lake residents identify themselves as Hispanic or Latino, and 7.8% identify as Asian  with a narrative report and a guide to interpretation. Several maps were used in a direct community interaction organized by the Iowa Cancer Registry. This event, "Cancer in Your Community," took place in Sioux City, Iowa, on September 21, 2006, drawing approximately 130 people. Dr Charles Lynch, director of the Iowa Cancer Registry and presenter at the event, noted that the spatially filtered maps were especially useful in his interaction with the public, as people come to an event such as his with an understanding of their geographic area, and seeing the local detail presented in the map helps the audience connect cancer rates with knowledge of their local areas, "pointing them in directions that they can take to alleviate problems they see" .The maps are distributed to the public through both the Internet and the ICCCC partners. The maps are posted on a Web site hosted by the University of Iowa (ICCCC partners gave the maps a positive reception at the ICCCC meeting in Des Moines, Iowa, on October 12, 2006, and the maps were featured in the July/August 2006 ICCCC newsletter . Jolene The difference between the maps created in this research and those traditionally produced to represent cancer rates by county are relevant to community participation. The continuously varying maps, in combination with the visualization of local context, provide local detail that cannot be achieved by using maps with large geographic units for displaying rates. These maps based on local data make the information about cancer rates more meaningful to people who are familiar with the local area. ICCCC partners have begun to use these maps in their work, and the collaboration continues.community and neighborhood for purposes of participation as well as data analysis, pitfalls of map interpretation, and the many cartographic choices that must be made. In addition, because we did not conduct a systematic assessment of our maps, future work could more systematically evaluate the degree to which these maps improve on traditional mapping by areal units from the perspective of cancer prevention and control practitioners and the public.This collaborative project is a start for research that integrates community participation and GIS for cancer prevention and control. Many issues must be considered in future work, including the tension between community involvement and health data confidentiality constraints, the structure of partnerships, the role of GIS, the potential for community-generated data and incorporation of local knowledge of "place," the potential for use of interactive computer- or Web-based mapping applications, the definition of terms such as As noted in the ICCCC newsletter, "Interpretation of this data will be the next step. For example, it will be important to determine if incidence rates are high in some areas due to increased screening or if rates are higher as a result of other factors" . Direct A limitation identified in the literature that should be noted in working with spatially adaptive filter maps and the public is the notion that rates are calculated for varying sizes of areas, and thus the spatial resolution is not consistent across the map ,22. ThusIndirectly adjusted rates have the limitation that comparisons across areas apply a different set of weights to each area, which reflects the age distribution of the area in question. However, for resource allocation purposes, the difference between actual and expected numbers of late-stage cancer cases is a measure of the need for additional resources such as screening services.By creating this initial set of maps and obtaining feedback from ICCCC partners and the public, we begin a dialogue about the types of questions communities are interested in and potential problems of interpreting the maps, and we can direct our attention to the development of tools and strategies that will allow us to address questions of interest and overcome problems. This mapping project is a beginning for further efforts to communicate and work with the public to alleviate cancer through efforts to change behaviors, investigate environmental exposures, and address disparities. The central goals of future work must be to improve scientific understanding of cancer rates and to promote effective, informed, and targeted intervention, involving the public in investigating community burdens of cancer at an appropriate scale and bringing the knowledge gained from this investigation into prevention and control activities.


This work explores the potential contribution of bioenergy technologies to 60% and 80% carbon reductions in the UK energy system by 2050, by outlining the potential for accelerated technological development of bioenergy chains. The investigation was based on insights from MARKAL modelling, detailed literature reviews and expert consultations. Due to the number and complexity of bioenergy pathways and technologies in the model, three chains and two underpinning technologies were selected for detailed investigation: (1) lignocellulosic hydrolysis for the production of bioethanol, (2) gasification technologies for heat and power, (3) fast pyrolysis of biomass for bio-oil production, (4) biotechnological advances for second generation bioenergy crops, and (5) the development of agro-machinery for growing and harvesting bioenergy crops. Detailed literature searches and expert consultations  led to the development of an 'accelerated' dataset of modelling parameters for each of the selected bioenergy pathways, which were included in five different scenario runs with UK-MARKAL (MED). The results of the 'accelerated runs' were compared with a low-carbon (LC-Core) scenario, which assesses the cheapest way to decarbonise the energy sector.Bioenergy was deployed in larger quantities in the bioenergy accelerated technological development scenario compared with the LC-Core scenario. In the electricity sector, solid biomass was highly utilised for energy crop gasification, displacing some deployment of wind power, and nuclear and marine to a lesser extent. Solid biomass was also deployed for heat in the residential sector from 2040 in much higher quantities in the bioenergy accelerated technological development scenario compared with LC-Core. Although lignocellulosic ethanol increased, overall ethanol decreased in the transport sector in the bioenergy accelerated technological development scenario due to a reduction in ethanol produced from wheat.There is much potential for future deployment of bioenergy technologies to decarbonise the energy sector. However, future deployment is dependent on many different factors including investment and efforts towards research and development needs, carbon reduction targets and the ability to compete with other low carbon technologies as they become deployed. All bioenergy technologies should become increasingly more economically competitive with fossil-based technologies as feedstock costs and flexibility are reduced in line with technological advances. Renewable energy is required as part of the future UK energy portfolio in order to meet CO2 reduction targets and improve energy security. An 80% reduction in CO2 emissions by 2050 coupled to the EU target of 15% supply of UK energy from renewables by 2020, represents ambitions that will require technology innovation, and better renewable deployment, as highlighted in the 2007 Stern Review [The UK Government states in the Energy White Paper 2007  that theThe IEA's Technology Perspective  draws at2 emissions, if it is produced in a sustainable way, and currently contributes approximately 80% of renewable power production in the UK [Bioenergy is one of the most prominent options to reduce COn the UK . Most ofn the UK . It is pn the UK .Bioenergy technologies are numerous and varied, incorporating many feedstocks, methods of conversion and supply routes to end products and end uses. In addition, bioenergy technologies can be found at all levels of maturity ranging from well established proven technologies, to new technologies that are in the research and development (R&D) phase. As a consequence, it is not possible to characterise the maturity of the bioenergy field as a whole. This can also partially explain why bioenergy research remains extensive and cross-disciplinary. Finally, such multi-disciplinarity and complexity means that it is not yet well understood how influential technological development will be for bioenergy's contribution to future low carbon energy systems.The main objective of this paper is to explore the possible contribution of emerging bioenergy technology to the UK energy system by 2050, by outlining the potential for accelerated technology development (ATD) of bioenergy systems in the UK. We aim to gain insight into how bioenergy technologies may contribute to meeting the 80% carbon reduction targets for a low carbon energy system in the UK, by using a MARKAL model complemented by relevant qualitative storylines highlighting key factors underpinning modelled technological development.et al. [et al. [The UK Energy Research Centre (UKERC) Energy 2050 project focuses on how the UK energy system may move towards a resilient, low-carbon system by 2050, while providing energy security . A fulleet al.  and the  [et al. .Bioenergy pathways are represented in MARKAL by more than 100 directly relevant technologies in the different modules of the model (and more than 200 indirectly relevant ones). Figure The methodology is summarised in Figure development of the bioenergy technology field, not accelerated deployment. Bioenergy is a wide field, representing a large number of chains with many feedstocks, conversions and supply routes that feed into heat, power and liquid biofuels in the UK [In order to explore accelerated technology development of bioenergy, this research focused specifically on accelerated n the UK  and it wn the UK . Based oThis was chosen because considerable technological advances are likely  and becaAlthough this is not a new technology, gasification was selected as it a process working towards deployment at demonstration and commercial scale and technology development is possible . This moThis is a technology largely at the early commercial stage; however, the production of transport fuels via fast pyrolysis is still in the R&D stage with potential for further advances . This exThis represents one of the underpinning technologies, as feedstock prices underpin many of the costs associated with bioenergy chains. The focus was on improvements through better breeding to advance dedicated second generation energy grasses and trees, not food crops. We focused on non-GM crops and domestic (UK) crops. The focus on domestic crops only was to reflect long-term environmental sustainability goals.The other underpinning technological improvement selected was the potential for improved machinery for growing and harvesting dedicated bioenergy crops. Good site preparation and weed elimination are highly influential on the performance of many energy crops, and improvements in these areas are important. There are also crop losses associated with inefficient harvesting/picking up of cut energy crops for example, which need to be addressed. Improvement in both agro-machinery and bioengineering of energy crops are likely to affect learning curves and supply costs for multiple bioenergy chains .The current status of each of the five chains and their potential for acceleration were assessed using data collected on both a qualitative  and a quantitative basis. This information was obtained from published literature, government reports and expert consultation.Qualitative information for the scenarios was used to estimate possible future technology developments until 2050, through processes such as gradual changes, step changes and innovation, as well as gaining an understanding of the possible milestones for each technology.The ATD bioenergy quantitative dataset was compiled for each of the five technologies using optimistic figures from literature searches and expert consultation to represent accelerated technology development from 2000 to 2050. The information available varied widely between the five technologies chosen. Accordingly, the assumptions and the process by which the data in the literature was used to determine the accelerated dataset are described below for each technology separately. The data collected consisted of figures on capital cost, operating and maintenance cost, technical efficiency, defined as the ratio between the useful output of energy conversion to the input, annual availability, defined as the share of the installed capacity that is used during a year (average share of the year), plant lifetime in the case of electricity generation and for biotechnological advances for second generation bioenergy crops, energy content and yield. Acceleration was represented through reducing costs, increasing efficiencies and including earlier availability for the technologies, in line with the literature and expert consultation. Since the UK-MARKAL database costs are in pounds sterling (GBP), all cost data were converted to GBP on a year 2000 basis.Once the data on accelerated technology development for selected bioenergy pathways was compiled, it was included in the modelling of five different 'accelerated' scenarios Table  to exploThis paper focuses on the contribution of bioenergy to decarbonising the energy system; however, further exploration of the other technology scenarios can be found in the forthcoming ATD report from UKERC Energy 2050 .The representation of technological development in MARKAL has been done by the introduction of technologies' vintages  with differing parameters corresponding to technological evolution, to represent learning effects or other advances in technology development. These parameters include capital cost, efficiency, operating and maintenance (O&M) costs, both fixed and variable, and where appropriate, availability, contribution to peak load, and plant life time.It is important to have an understanding of the R&D needs of a technology pathway when assessing its potential for accelerated technology development. Understanding the major hurdles to development and deployment is also critical when considering the likelihood of technology breakthroughs and step changes within a technology pathway.Bioenergy is diverse and flexible, covering many feedstock resources, conversion pathways and outputs . As suchThe development of new dedicated bioenergy crops for feedstocks is one of the most fundamental R&D needs for bioenergy, as this underpins the development and cost of many bioenergy conversion technologies . The UKEImproved establishment of dedicated bioenergy crops on marginal and idle land, as recommended by the Gallagher Review  would alAdvances in site preparation, weed elimination  and imprTechnical improvements in existing conversion technologies such as gasification, and novel conversion technologies like fast pyrolysis, are also needed. R&D needs for both of these technologies include increasing conversion efficiency, reducing overall technology costs, increasing fuel flexibility so that a variety of new energy crops can be utilised as feedstocks and improving product quality through gas cleaning in gasification and producing cleaner bio-oil from fast pyrolysis ,18,21-25in-situ enzyme systems for wall disassembly [The economic competitiveness of biofuels compared with conventional fuels is a key barrier in the deployment of biomass in the transport sector . In ordeassembly ,26. Bettassembly  to make Table Major technology improvements and accelerated development which will reduce overall costs and increase efficiency of lignocellulosic conversion to ethanol are expected from a combination of improvements in feedstock quality, with reduced lignin for better breakdown of cell walls, cheaper enzymes and more efficient conversion technologies, which require less pre-processing, plus an improvement in the fermentation process Table .Parameters changed within MARKAL included capital costs and O&M costs. All other costs associated with this technology were kept the same as the core scenario. The parameters used to model lignocellulosic ethanol conversion were changed as follows to represent accelerated development Table .Lignocellulosic ethanol is available in the model from 2010. In the core scenario, it is modelled with an annual availability of 100%, which was reduced slightly to 90% in the accelerated scenario.et al. [In the core scenario the efficiency is modelled as 90%. Although this figure is too high, with expected efficiencies to be around 30 to 40% , the efficiency was kept at 90% in order to keep it comparable to the non-accelerated scenario and to avoid any drastic model 'deceleration', as this would be contrary to the aim of the exercise. Laser et al.  suggeste-1, which is in line with recent US Department of Energy (DOE) research [-1 by 2050. These changes in investment costs are likely to occur as economies of scale are obtained when it is possible to construct larger plants  .The investment costs of lignocellulosic ethanol conversion in the core scenario are 23 GBP.GJresearch . In the -1) seems too high. In the accelerated scenario, O&M costs were reduced to 5% of the investment costs in 2000 and 2010, and to 2% of the total investment costs by 2050. These percentages are in line with expert analysis and estimates on expected development of the technology .For the variable O&M costs, the data used in the core scenario   (a forerEnergy crop gasification is represented in MARKAL by existing gasification (2000) and a number of technology 'vintages' in the model, including one at 2010, 2020, 2030 and 2040. The following data were used for the ATD gasification scenario , but was increased to 47% in 2010 and 2020, and 50% in 2030 and 2040 in the accelerated scenario .e-1, reducing to 1,450 GBP.kWe-1 in 2010, and 700 GBP.kWe-1 by 2020 as reported in the DTI [Capital costs for the accelerated scenario were kept the same in 2000 as in the core scenario at 2,200 GBP.kW the DTI  and coule.yr)-1, decreasing to 51.5 GBP.(kWe.yr)-1 in 2010 and to 30 GBP.(kWe.yr)-1 by 2030 to 2040 to be consistent with figures reported in the DTI ecomomics report and NREL powerbook [Under the accelerated technology scenario, the fixed O&M costs in 2000 were kept at 66 GBP.  and the literature available, the costs were kept the same for both the core and accelerated scenario in 2005 at 32.4 GBP.GJ-1. In the accelerated scenario, however, O&M costs were reduced after 2000 to represent a figure of 4% of the capital costs, dropping to 1 GBP.GJ-1 by 2050 (to be consistent with the literature [The efficiency of fast pyrolysis in the accelerated scenario did not change from the 90% found in the core scenario. Variable O&M costs were modelled in 2000 in both scenarios at 3 GBP.GJterature ).Acceleration of bioengineering of energy plants focused on domestic energy crops (within the UK) to reflect the environmental sustainability criteria. Imported energy crops were not accelerated. Improvements in the yield of energy crops are predicted to be the major factor that will accelerate the development of energy crops Table , and theA literature review of energy crop costs highlighted the wide range of plants suitable as bioenergy crops. Data obtained for this scenario, however, focused only on those crops which are suitable to be grown in the UK .-1) using an assumption of average yield of 12 t.(ha.yr)-1 increasing to a future yield of 24 t.(ha.yr)-1 in 2050.Although there are a wide variety of bioenergy crops with different crop costs, MARKAL uses an average figure to represent all energy crops. The estimates for crops from the literature e.g. -34 there-1, but in the ATD scenario this was decreased to 2.9 GBP.GJ-1 in 2010 and 1.45 GBP.GJ-1 by 2050, to represent a gradual improvement in biotechnology from 2000 to 2050 . Although energy crops are utilised in both scenarios in 2010, there is a much larger uptake of bioenergy crops across all vintages in the ATD scenario from 2010 to 2050 Figure . The lanThe production of electricity from biomass (primarily from gasification) reaches a peak of 277 PJ of electricity  in 2035 in the ATD scenario, compared with a peak of only 62 PJ in the LC-Core in 2025 Figure . This inIn the ATD scenario, the use of solid biomass for electricity generation increases until 2040, when it reaches 481 PJ of energy crops. After 2040 the use of energy crops for electricity generation decreases, reaching 284 PJ by 2050 .The deployment of gasification in the ATD scenario also has some smaller effects on the levels of adoption of other bioenergy technologies. For instance, biomass district heating technologies (heat only) in the ATD Bioenergy scenario are used more than in the LC-Core scenario in the short term, but significantly less in the longer term. In addition, the deployment of gasification also means that biomass CHP plant (LTH) is never deployed in the ATD scenario whereas it was deployed from 2035 onwards in the LC-Core scenario.Accelerated technology development of bioenergy creates changes in the residential heating sector when compared with the LC-Core scenario Figure . There iIn the service sector, woodchips are displaced by pellets (from energy crops) from 2040 onwards in the ATD scenario, unlike the LC-Core scenario where wood is used until 2050. The increasing use of energy crops for the residential and service sectors in the long term corresponds to the timing of the declining use of energy crops for electricity and the continuing increase in production of energy crops.Overall, final energy demand from biomass in the transport sector does not differ significantly between the LC-Core and ATD Bioenergy scenarios. The total transport fuel demand is the same in both scenarios until 2035 to 2040, when the total transport fuel demand in the ATD is slightly higher (152 PJ) than in the LC-Core (142 PJ) Figure . Howevervs. 103 PJ in ATD Bioenergy). However, there is an increase in the uptake of the accelerated lignocellulosic ethanol in the ATD scenario from 2035 onwards (this technology was not deployed after 2035 in the LC-Core scenario). However, the increase in lignocellulosic ethanol is smaller than the decrease in traditional wheat ethanol and therefore there is an overall reduction in the level of ethanol in the ATD scenario.The overall impact of acceleration on ethanol is negative because less domestic ethanol is produced in the ATD scenario. Imported ethanol remains at similar levels in both scenarios. There are two pathways for the production of ethanol in MARKAL: traditional straw fermentation and lignocellulosic conversion to ethanol. In the ATD scenario, traditional ethanol from wheat straw fermentation is deployed later and at lower levels  scenario at 60% carbon reduction, there is less biomass for electricity after 2040 than there was in ATD Bioenergy Figure . This isWhen accelerating all the technologies together at an 80% carbon reduction, there are again major changes to the distribution of biomass. While there are still high levels of biomass being utilised in LC Acctech (no fuel cells) (80%), the biomass is being distributed to the sectors differently.In LC Acctech (no fuel cells) 80%) there is much less biomass deployed for electricity production (a peak of 150 PJ as opposed to 290 PJ in LC Acctech (no fuel cells) (60%)  are accelerated for a 60% carbon reduction target then bioenergy is used more for electricity generation than in any other scenario (including the single technology acceleration-ATD Bioenergy) Figure . HoweverWhen all the technologies (including fuel cells) are accelerated with an 80% carbon reduction target then biomass is used less for electricity generation than in the other accelerated scenarios Figure . TranspoThis study highlights the potential for innovation throughout the bioenergy supply chain to contribute to the decarbonisation of multiple sectors of the UK energy system. Based on the research narratives developed and techno-economic modelling scenarios, the results suggest that bioenergy has the potential to be an affordable option to decarbonise not only the electricity and residential heat sectors, but also the transport sector under an 80% carbon reduction target given further technological development.When bioenergy technologies are accelerated in isolation in the ATD Bioenergy scenario, electricity production from biomass is highly deployed in the medium term followed by increased residential heat from pellets (energy crops) in the long term. Until 2035, a similar pattern was seen for electricity generation from biomass in the aggregated scenario (LC Acctech (60%) with fuel cells) with a 60% carbon reduction. However, in the 60% aggregated scenario without fuel cells, there was less electricity production from 2040 to 2050 and much more biomass for residential heating than in the ATD Bioenergy scenario. This suggests that in the aggregated scenarios, electricity produced from bioenergy crops becomes less economically competitive than other accelerated low carbon electricity options such as marine, wind power and solar PV. The flexibility of bioenergy means it can be used in multiple end use sectors, while the other renewables cannot. Biomass therefore becomes better used as a low cost option to decarbonise residential heating in the aggregated scenarios when competing with other renewables.Under the increased carbon reduction targets in the aggregated 80% scenario without fuel cells (LC Acctech (80%) no fuel cells scenario), the distribution of biomass changes. There is significantly less electricity and residential heat generated from biomass but more biomass in the transport sector (biofuels) in the 80% scenarios. The higher carbon reduction target of 80% results in more pressure on the transport sector to decarbonise compared with the 60% scenarios. Without fuel cell acceleration, there are few affordable options to decarbonise transport and biofuels is the cheapest option. Therefore the model shifts much of the biomass away from electricity and heat and towards transport biofuels. This suggests that with a limited resource like biomass there should be a thorough investigation into the optimal utilisation of the resource to decarbonise the economy.When fuel cells, an alternative option for decarbonising the transport sector, are introduced in LC Acctech with fuel cells at a 60% or 80% carbon reduction, the fuel cells are deployed for decarbonisation of the transport sector. In the 60% scenario this leads to biomass being used primarily for electricity generation and heat while in the 80% scenario biomass is used earlier for residential heating. This reinforces the message that the optimal distribution of biomass depends on the ambition of the carbon target and the availability of alternative low carbon technologies. A whole system mentality must be used when determining how to best use biomass resources.Given the importance of low cost biomass resources in the increased uptake of bioenergy in the ATD Bioenergy scenario, cheaper feedstocks are clearly important for the future deployment of bioenergy technologies. This suggests that much of the scope for accelerated deployment of bioenergy comes from the development of more efficient, low cost energy crops. This can be achieved through increasing the yield, crop resistance to disease and pest species and by increasing successful establishment of perennial species. Feedstock flexibility is also important for many of the bioenergy technologies, and therefore improvements in this area will increase the economic competitiveness of bioenergy.The results from all the scenarios suggest that using biomass for residential heat is a potential option to decarbonise the UK's energy market when bioenergy is competing with other accelerated technologies under 60% carbon reduction targets. However, in 80% carbon reduction scenarios, transport biofuels are deployed at much higher rates. This suggests that to achieve a higher level of decarbonisation, transport will need to be highly decarbonised and that lignocellulosic ethanol could be one way to achieve this. Given the uncertainties surrounding the ATD figures and our 'what-if' rather than a predictive approach, however, it remains to be seen whether bioenergy technologies will develop and be deployed in this way.Fast pyrolysis for bio-oil production was not deployed in any of the scenarios. This certainly does not mean that fast pyrolysis technology is without potential. To fully understand why fast pyrolysis was not deployed, a sensitivity analysis would need to be undertaken on the costs of pyrolysis technology within the model; however, due to time constraints this was not possible. It is additionally important to highlight that the model may not capture some key advantages in using fast pyrolysis in an energy system which are beyond cost competitiveness. MARKAL is used for 'what-if' analysis and focuses on 'least-cost' solutions for the energy system over the time horizon chosen. Consequently, MARKAL will select the technologies which supply energy at the lowest cost, even if this only represents a marginal cost saving. One of the consequences of this modelling paradigm is that some technologies/pathways may not be selected by the model as part of the 'optimal' energy system configuration even if in reality they could be developed. In addition the MARKAL modelling framework can only capture some of the 'non-economic' benefits of certain energy technologies/pathways, which influences its choice of 'solutions'. Although the UKERC 2050 MARKAL model has, additionally, been thoroughly tested , it has not been built specifically to explore bioenergy pathways. Within the time constraints of the project, it was not possible to improve the bioenergy chains represented within the model. Within the TSEC-BIOSYS modelling exercise, however, bioenergy chains were specifically improved. 'Domestic' fast pyrolysis also was not deployed in the system; however, imported bio-oil was deployed most notably in the industrial sector within the TSEC-BIOSYS model (unpublished data). The 'imported' bio-oil pathway is currently not modelled within the MARKAL model used for UKERC 2050 and this highlights an area where the model needs to be improved.Land availability within the UK for growing bioenergy crops is, additionally, a big issue within the bioenergy field. In MARKAL, the upper bounds of available energy crops were capped to reflect the limited availability of land for biomass in the UK. However, there are also other issues associated with bioenergy that could further limit biomass levels in the UK. Bioenergy is often considered controversial due to issues surrounding direct and indirect land use change ,35, realA further key factor in determining the use of UK land for energy crop production will be the availability and price of imported biomass. This is important given that at least half of the current biomass supply is sourced from outside the UK  and thatSocial and environmental limitations on bioenergy development and deployment, such as the wide-scale environmental costs and benefits of bioenergy deployment on ecosystem services, and direct and indirect land use displacement, may make deployment of bioenergy technologies challenging. Overall, however, the work suggests that bioenergy can contribute significantly to a low carbon UK energy future. However, it is important to keep in mind that (1) the modelling overlooks many barriers to development and deployment other than costs; (2) the modelling does not properly model some key aspects linked to bioenergy infrastructure, both spatial and temporal, and (3) the figures used in the ATD scenarios are uncertain and should be taken with caution. This work offers insights into the potential accelerated technology development and deployment that could occur along selected bioenergy chains in the UK. It should be taken as an illustration, rather than a prediction, of how bioenergy could be deployed in the future UK energy system. The analysis undertaken contributes mostly from the illustration of 'what could be done' with MARKAL to look into the potential of ATD and bioenergy.Our findings are limited by the uncertainty on the values chosen to represent future ATD. For example, the results for the deployment of lignocellulosic ethanol are based on very high conversion efficiencies, and should be taken with caution, and not as a prediction of the capability of lignocellulosic technology deployment. Rather, the results from this should be looked at as an illustration of the capability of the model, and as highlighting where further work is needed. The UK-MARKAL database is iteratively constructed and improved, and this has been corrected for further runs. However, as a consequence of time constraints, it was not possible to include this revised value for the ATD runs for the present work.In addition, the focus was mostly on one scenario. A single ATD Bioenergy scenario was modelled in MARKAL which combined the accelerated development input data of the five bioenergy chains that were selected for exploration. Although the use of different scenarios would help to account for some of the uncertainties in the figures used in the scenarios, this was not possible given the time constraints of the project. In future, these uncertainties would need to be taken into account, for instance, by using different scenarios, including the use of more bioenergy chains, and by undertaking sensitivity analyses to determine which parameters are most influential on the deployment of the bioenergy technologies explored.Moreover, the scenarios focused on five select technologies/bioenergy chains. There are other promising bioenergy technologies which have the potential to be economically viable and sustainable, especially those where active research is being conducted both in the UK and internationally. Some technologies, like algal fuels, have potential but are currently not represented within MARKAL.It is also important to highlight that the failure of a technology to be deployed in these scenarios does not mean that technology is without potential. As MARKAL is a least-cost optimisation model, it will select the cheapest technology to serve the demand while satisfying the constraints, even if those cost savings are only marginal.Although the UKERC 2050 MARKAL model has, additionally, been thoroughly tested , it has not been built specifically to explore bioenergy pathways. Consequently, this means our approach is challenging, but innovative nonetheless. Here the authors have illustrated the possibilities of the model, but within the time constraints of the project, it was not possible to improve the bioenergy chains represented within the model. The 'imported' bio-oil pathway is currently not modelled within the UKERC 2050 model and this highlights an area where the model needs to be improved.The costs used in the modelling were additionally calibrated on a 2000 base year and we are conscious of the limitations of this approach and the possibility of improvement to represent more carefully the 'short term'. The value of the modelling exercise, however, may be more in exploring the longer term trends.Further work is needed to build on our proposed approach and it could be useful to systematically look at the pathways to answer the question 'how much improvement is necessary in the different biotechnologies before they can be expected to have a significant role in the future energy system?'This paper has highlighted the applicability of an original modelling approach, but future work should be focused to address some of the above limitations. It is also important to note that the modelling has been underpinned by clear qualitative stories, including R&D directions and potential and policy implications, which give the modelling results context. The UK-MARKAL model is an 'iteratively built' database and model and by highlighting its possibilities as well as current gaps in data representation and/or capabilities, we continue to contribute to the future improvements of the model.This work explores the potential of bioenergy technologies to contribute to carbon reductions in the UK energy system through accelerated technology development. The analysis undertaken represents an illustration of 'what-if' scenarios in MARKAL to explore accelerated technology development of bioenergy technologies.The exercise has highlighted that there is much potential for accelerated technology development in the five bioenergy technologies investigated in this paper, particularly in bio-engineering of energy crops as it underpins many bioenergy chains. Given further development, bioenergy technologies could become increasingly more economically competitive with fossil-based technologies as feedstock costs are reduced in line with crop improvements due to plant breeding efforts, the ability to grow energy crops on marginal lands, increased crop resistance to disease and pests, cheaper enzymes for lignocellulosic conversion to bio-ethanol, and improvements in gasification and fast pyrolysis technologies. There is additional potential for advances in other bioenergy technologies not assessed in this paper, which could help to drive the commercial availability and competitiveness of bioenergy technologies in the R&D stage further forward.This paper highlights the unique flexibility of bioenergy technologies to potentially decarbonise multiple sectors. Under all the scenarios there was a high deployment of bioenergy, which implies that it is possible that bioenergy will be a valuable part of the pathway to a decarbonised economy. It is important to highlight, however, that figures used in the ATD scenario were uncertain and should be taken as an illustration of how much improvement is needed in the five technologies for the levels of market penetration seen in the model output. Interestingly, carbon reduction targets influenced the bioenergy mix deployed in the UK energy market. Lower targets (60%) resulted in more electricity and residential heat, while higher targets (80%) resulted in increased deployment of biomass for biofuels. Acceleration of bioenergy without other technologies accelerated, however, led to more electricity from biomass because other low carbon electricity options were less cost competitive.Innovation at all stages of the bioenergy supply chain is important and can contribute to increased chance of deployment. Future R&D efforts and innovation are therefore essential at all points along the supply chain.Ultimately, the future deployment of bioenergy technologies is dependent on many different factors including investment and R&D efforts, carbon reduction targets and the ability to compete with other low carbon technologies as they become deployed.ATD: accelerated technological development; BERR: Department for Business, Enterprise and Regulatory Reform; CCC: Committee on Climate Change; DTI: Department of Trade and Industry; GBP: pounds sterling; NREL: National Renewable Energy Laboratory; O&M: operating and maintenance; R&D: research and development; RD&D: research, development and demonstration; UKERC: UK Energy Research Centre.The authors declare that they have no competing interests.DC collected quantitative and qualitative data for most of the chains, interpreted the results and drafted the manuscript. SJ collected quantitative and qualitative data for the lignocellulosic chain, and helped to draft the manuscript. BM helped with data collection, interpreted the results and also drafted the manuscript. GA undertook the modeling work and commented on the manuscript. GT helped draft the manuscript. All authors read and approved the final manuscript.


Recent years have seen an increased amount of natural language processing (NLP) work on full text biomedical journal publications. Much of this work is done with Open Access journal articles. Such work assumes that Open Access articles are representative of biomedical publications in general and that methods developed for analysis of Open Access full text publications will generalize to the biomedical literature as a whole. If this assumption is wrong, the cost to the community will be large, including not just wasted resources, but also flawed science. This paper examines that assumption.We collected two sets of documents, one consisting only of Open Access publications and the other consisting only of traditional journal publications. We examined them for differences in surface linguistic structures that have obvious consequences for the ease or difficulty of natural language processing and for differences in semantic content as reflected in lexical items. Regarding surface linguistic structures, we examined the incidence of conjunctions, negation, passives, and pronominal anaphora, and found that the two collections did not differ. We also examined the distribution of sentence lengths and found that both collections were characterized by the same mode. Regarding lexical items, we found that the Kullback-Leibler divergence between the two collections was low, and was lower than the divergence between either collection and a reference corpus. Where small differences did exist, log likelihood analysis showed that they were primarily in the area of formatting and in specific named entities.We did not find structural or semantic differences between the Open Access and traditional journal collections. For much of the modern period of biomedical natural language processing (BioNLP) research, work in text mining has focused on abstracts of journal articles. Free and widely available via PubMed/MEDLINE in numbers previously unseen in most statistical text mining work, abstracts enabled a mass of work that has grown remarkably quickly . In receAt the same time, it became clear very early on that full text poses challenges that are different from those of abstracts. For example, Tanabe and Wilbur (2002) found that some sections  tend to produce much higher rates of false positives on information extraction tasks than others . FurtherThese problems notwithstanding, recent years have seen an increased emphasis on working with full text papers  is a document collection that we assembled from traditional subscription-based journals, with the intent of collecting a set of texts that topically parallels the CRAFT corpus as closely as possible. This parallelism was achieved via shared Gene Ontology annotations (see the Methods section). TraJour consists of 99 articles and almost 600K words.• Reference is a corpus based on the the Wall Street Journal corpus. This is a collection of newspaper articles that has been extensively annotated in the course of the Penn Treebank [• Treebank  and PropTreebank  projectsBioReference is a document collection which aims to be representative of full text biomedical publications in general, rather than being tailored to mouse genomics. It was constructed from a random subsample of two document collections: the TREC Genomics Corpus [• s Corpus , containWe compared the corpora according to various surface-level characteristics as well as several linguistic phenomena. We performed comparisons of the statistical properties of the vocabularies of the corpora in order to identify important variations of language use among them. The two corpora of primary interest are the two semantically comparable corpora – CRAFT, our open access publication corpus, and TraJour, our traditional journal corpus.We examined the incidence of a number of morphosyntactic/semantic phenomena in the four sets of documents. We selected them because each is known to have consequences for natural language processing: in particular, all of the morphosyntactic phenomena that we examined make the text mining task more difficult by introducing complexity and variability in the linguistic structures found in the text. The linguistic phenomena that we examined were negation, passivization, conjunction, and pronominal anaphora.no, not, neither, and nor, as well as the affix n't. To examine passivization, we counted instances of the strings ed by, en by, and ound by. This clearly underestimates the number of passives. For example, conjoined passive verbs, as in eEF2 kinase is phosphorylated and inhibited by SAPK4/p38 delta [MAPK is activated primarily by FGF in this context [by-phrase indicating the agent). However, it yields a reasonable approximation of the number of passives, and the undercounting applies proportionally to all four document sets, so the intra-corpus comparison probably remains valid, although we would need to do a separate analysis to verify this. To examine conjunction, we counted every instance of and, or, and but not. Finally, to examine pronominal anaphora, we counted every instance of any pronoun. In each case, we normalized the counts by the number of words in the corpus.To examine negation, we counted every instance of the words 38 delta , will be context , will caTable The directions of the differences with the reference corpus are mostly not surprising. Passives are more common in the two semantically matched corpora (0.39% and 0.43%) and in the BioReference (0.48%) than they are in the Reference corpus (0.24%). This accords with the observation that passives are almost caricatural of scientific writing and are quite common in biomedical language .Conjunctions are more frequent in the scientific corpora than in the reference corpus. As Biber et al.  point oualthough, however, nevertheless, and on the other hand [The pattern of incidence of negations is also in line with other contrastive reports of negation in the academic and news registers . Incidenher hand (81–82).We measured the distribution of sentence lengths because sentence length has implications for syntactic parser performance. Parser accuracy falls as sentence length increases: thus, if there were a difference in sentence lengths between the CRAFT and TraJour corpora, that would indicate that one would present more challenges than the other for an important class of linguistic analysis. Figure The preceding measures are all concerned with linguistic  or structural (sentence length) feature distributions and their implications for processing difficulty. We now turn to measures that are more reflective of the semantic content of the corpora.To further explore the possibility of important differences between CRAFT and TraJour, we looked at two measures of lexical difference and similarity. The first of these is Kullback-Leibler divergence , or relaw in the vocabulary V formed by combining the sets of unique words in two corpora c1 and c2. It is calculated as shown in equation (1), and it is converted to a symmetric distance with equation (2).Kullback-Leibler divergence measures the divergence between two probability distributions. Here, we consider the probability of each word n most frequent words, from the 100 most frequent words to the 10,000 most frequent words, comparing the probability distributions for those selected words in the two corpora. We employed Laplace (add-one) smoothing to accommodate for words which occurred in one corpus but not in the other.Intuitively, as two distributions become more different, the value for KL divergence increases. We assume a threshold value of 0.005 corresponds to near identity of the distributions. We calculated the KL divergence between CRAFT and TraJour and between each of the two and the reference corpora. We ordered words by frequency in the merged vocabulary of the corpora and then calculated the KL divergence for different values of the top Figure In contrast, if either corpus is compared against the reference corpus, they are drastically different, with KL divergences for the top 100 words of 0.161 and 0.167, respectively – far above the assumed identity threshold. Even compared with the BioReference corpus, the divergence is well above this threshold (0.044 and 0.021 @100 words), suggesting that there are significant lexical differences between the mouse genome corpora and general biomedical text, while there do not appear to be lexical differences simply due to the mode of publication of the text.t in corpus i, where Ni is the number of word types in corpus i and Oi is the number of occurrences of t in corpus i. It is calculated as shown in equations (3)–(4), with (3) representing the expected value for a term in corpus i, and (4) the log likelihood for that term. Ei tells us how many instances of the term we would expect to see in corpus i if the occurrences were evenly distributed across the two corpora. The Log Likelihood measures how far off from that ideal the actual occurrences are. This measure is argued by [KL divergence scores indicate that CRAFT and TraJour differ very little with respect to semantic content; analysis of the log likelihood scores helps us understand where precisely the two scientific corpora do differ. It will be seen that much of the difference between them is due to formatting and to named entities. Log likelihood values uncover terms that distinguish one corpus from another, by identifying terms that have the most significant relative frequency difference . For eacrgued by  to be prWe see the results of the log likelihood analysis in Tables We can analyze this data in terms of two characteristics: the magnitude of the differences, and the semantic nature of the words in terms of which the various pairs of corpora differ.figure, with a log likelihood of 2318.9, while the most different word between CRAFT and BioReference is mice with a log likelihood of 3755.8. The most different word between TraJour and BioReference is mouse, with a log likelihood of 1260.6.  With respect to the semantic content of the words in terms of which the various pairs of corpora differ, we see clear patterns. The six most different words between the two semantically matched corpora CRAFT and TraJour all reflect formatting: figure and doi, which are overrepresented in CRAFT as compared to TraJour, and window, fig, text, and abstract, which are overrepresented in TraJour. In fact, of the 50 most different terms between the two corpora, at least a quarter of them reflect formatting differences and artifacts of the text conversion routines – the preceding six terms, plus pp, ?m, °c, null, -1?, -1??, and 5?. Many of the remaining differences are due to the specific named entities that occur in each corpus. However, when we compare either of the two semantically matched corpora CRAFT and TraJour against BioReference, we see content words such as mice, mouse, and embryos ranked much higher, and we see more overlap among the most significant terms. In Table With respect to the magnitude of differences, we see that the most different words in the two content-matched corpora, CRAFT and TraJour, are far less different than the most different words between either of those corpora and either of the reference corpora: the most different word between CRAFT and TraJour is do differ from these more general document sets, indicating that if the Open Source and traditional journals did differ from each other, our methods would have uncovered those differences.In terms of linguistic phenomena such as conjunction, passivization, negation, and pronominal anaphora, the content-matched Open Source and traditional publications do not differ from each other. They also do not differ in terms of sentence length. When compared against reference corpora, they The two target corpora analyzed (CRAFT and TraJour) are both in the molecular biology domain, and more specifically mouse genomics. As such, the results and conclusions, strictly interpreted, apply only to the particular datasets we examined. Based on the analysis of the factors that might lead to textual variation (see Background), it would be conservative to assume that these results generalize to the molecular biomedical literature as a whole. We believe that generalizing these results to the entire biomedical literature, or even all peer reviewed scientific publications, is reasonable, although additional testing may be warranted for areas with substantially different cultures of scientific practice.We tried hard to find differences between the CRAFT and TraJour document sets. We mostly failed. Research on Open Access documents applies to traditional, subscription-only journals.The document set was selected by collecting the set of Gene Ontology annotations with an evidence code of Traceable Author Statement (see Gene Ontology 2000 for an explanation of evidence codes) from the Mouse Genome Institute's Gene Ontology annotation file  for docuAmerican Journal of Epidemiology to several American Journal of Physiology journals (e.g. Heart and Circulatory Physiology), and as such the corpus has quite broad coverage of biomedicine despite the "Genomics" name. Our selection included 41 articles from The Journal of Biological Chemistry, 12 from Blood, 4 each from Human Reproduction, Human Molecular Genetics, and the Journal of Applied Physiology, and 1–3 each from 20 other journals.One hundred PubMed identifiers were selected at random from each of two sources: the 2006 TREC Genomics Corpus  and the Nucleic Acids Research (23 articles), Environmental Health Perspectives (9 articles), Ulster Medical Journal (4 articles), BMC Genomics (4 articles), Medical History (4 articles) and 44 other journals contributing 1 or 2 articles each.The portion of the BioReference corpus randomly selected from the PubMed Central Open Access included publications from Three of the articles selected for the PubMed Central dataset were missing from that set. After selecting the files and pre-processing them to extract the plain text, two files from the TREC Genomics collection were found to be empty. The corpus thus consists of 195 files containing content, 97 from the PubMed Central Open Access dataset and 98 from the TREC Genomics dataset. We then eliminated any files less than 1 kb (1024 bytes) in length, as those did not represent full text files. The remaining 163 files comprise a reference set which can be considered to be a balanced sample of both full text Open Access and traditional journal publications indexed in PubMed, and are not oriented on the topics relevant to mouse genomics on which CRAFT and TraJour are focused.All the computations described here were implemented in Java within the UIMA (Unstructured Information Management Architecture) ,23 frameWe have not performed significance testing of the statistical results provided in this paper as we are mostly interested in the qualitative differences that could impact text mining applications, and minor variations will always exist between any particular document corpora. This is a limitation of the approach.KV conceived the lexical distribution measures, collected and pre-processed the corpora, and designed and carried out the KL divergence, frequency, and log likelihood experiments. KBC conceived, designed, and carried out the linguistic/syntactic experiments. LH contributed to the design of the experiments. KV, KBC, and LH analyzed the results and wrote the paper.