Inside the radar: select elements in nanomaterials and sustainable nanotechnology

Rare earth elements and several metals formerly uncommon in commerce are being introduced into new products, particularly as nanoscaled materials. Until recently, little attention has been paid to their sustainability issues. This perspective addresses these elements, their commercial uses, health and environment issues, sustainability, and suggests a path forward.     

Conservation Priorities and Population Structure of Woody Medicinal Plants in an Area of <Emphasis Type="Italic">Caatinga</Emphasis> Vegetation (Pernambuco State,NE Brazil)

In spite of heavy harvesting pressure on some of the most popular medicinal plant species, there are very few published studies concerning their conservation the semi-arid region of northeastern Brazil. In light of this fact, the present work sought to evaluate the local conservation and the harvesting sustainability of medicinal plants in an region of caatinga vegetation employing a fusion of biological and cultural approaches. Ethnobotanical methodologies and techniques were employed in the community of “Riachão de “Malhada de Pedra”” (municipality of Caruaru, state of Pernambuco, Brazil) in order to document local knowledge concerning medicinal plants and to examine the availability of those plants in a caatinga vegetation fragment located near that community. A total of 21 medicinal plant species were identified in the area and classified according to ecological factors and local uses. Two plants (Ziziphus joazeiro and Myracrodruon urundeuva) stood out has having high priority for conservation efforts. Sixteen species were identified as having populations adequate for harvesting through a system of pre-determined quotas, while four species were deemed sufficiently abundant to be harvested without risk of causing significant impact on their sustainability.     

Modeling of SO<Subscript>2</Subscript> distribution in Istanbul using artificial neural networks

Urban air pollution is a growing problem in developing countries. Some compounds especially sulphur dioxide (SO₂) is considered as typical indicators of the urban air quality. Air pollution modeling and prediction have great importance in preventing the occurrence of air pollution episodes and provide sufficient time to take the necessary precautions. Recently, various stochastic image-processing algorithms such as Artificial Neural Network (ANN) are applied to environmental engineering. ANN structure employs input, hidden and output layers. Due to the complexity of the problem, as the number of input–output parameters differs, ANN model settings such as the number of neurons of these layers changes. The ability of ANN models to learn, particularly capability of handling large amounts (or sets) of data simultaneously as well as their fast response time, are invariably the characteristics desired for predictive and forecasting purposes. In this paper, ANN models have been used to predict air pollutant parameter in meteorological considerations. We have especially focused on modeling of SO₂ distribution and predicting its future concentration in Istanbul, Turkey. We have obtained data sets including meteorological variables and SO₂ concentrations from Istanbul-Florya meteorological station and Istanbul-Yenibosna air pollution station. We have preferred three-layer perceptron type of ANN which consists of 10, 22 and 1 neurons for input, hidden and output layers, respectively. All considered parameters are measured as daily mean. The input parameters are: SO₂ concentration, pressure, temperature, humidity, wind direction, wind speed, strength of sunshine, sunshine, cloudy, rainfall and output parameter is the future prediction of SO₂. To evaluate the performance of ANN model, our results are compared to classical nonlinear regression methods. The over all system finds an optimum correlation between input–output variables. Here, the correlation parameter, r is 0.999 and 0.528 for training and test data. Thus in our model, the trend of SO₂ is well estimated and seasonal effects are well represented. As a result, we conclude that ANN is one of the compromising methods in estimation of environmental complex air pollution problems.     

Industrial point source CO<Subscript>2</Subscript> emission strength estimation with aircraft measurements and dispersion modelling

CO₂ remains the greenhouse gas that contributes most to anthropogenic global warming, and the evaluation of its emissions is of major interest to both research and regulatory purposes. Emission inventories generally provide quite reliable estimates of CO₂ emissions. However, because of intrinsic uncertainties associated with these estimates, it is of great importance to validate emission inventories against independent estimates. This paper describes an integrated approach combining aircraft measurements and a puff dispersion modelling framework by considering a CO₂ industrial point source, located in Biganos, France. CO₂ density measurements were obtained by applying the mass balance method, while CO₂ emission estimates were derived by implementing the CALMET/CALPUFF model chain. For the latter, three meteorological initializations were used: (i) WRF-modelled outputs initialized by ECMWF reanalyses; (ii) WRF-modelled outputs initialized by CFSR reanalyses and (iii) local in situ observations. Governmental inventorial data were used as reference for all applications. The strengths and weaknesses of the different approaches and how they affect emission estimation uncertainty were investigated. The mass balance based on aircraft measurements was quite succesful in capturing the point source emission strength (at worst with a 16% bias), while the accuracy of the dispersion modelling, markedly when using ECMWF initialization through the WRF model, was only slightly lower (estimation with an 18% bias). The analysis will help in highlighting some methodological best practices that can be used as guidelines for future experiments.     

Short-Term Forecasting of Nitrogen Dioxide (NO<Subscript>2</Subscript>) Levels Using a Hybrid Statistical and Air Mass History Modelling Approach

A novel hybrid model has been developed to support the provision of real-time air quality forecasts. Statistical techniques have been applied in parallel with air mass history modelling to provide an efficient and accurate forecasting system with the ability to identify high NO₂ events, which tend to be the episodes of most significance in Ireland. Air mass history modelling and k-means clustering are used to identify air mass types that lead to high NO₂ levels in Ireland. Trajectory matching techniques allow data associated with these air masses to be partitioned during model development. Non-parametric regression (NPR) has been applied to describe nonlinear variations in concentration levels with wind speed, direction and season and produce a set of linearized factors which, together with other meteorological variables, are employed as inputs to a multiple linear regression. The model uses an innovative integrated approach to combine the NPR with the air mass history modelling results. On validation, a correlation coefficient of 0.75 was obtained, and 91 % of daily maximum (hourly averaged) NO₂ predictions were within a factor of two of the measured value. High pollution events were well captured, as indicated by strong agreement between measured and modelled high percentile values. The model requires only simple input data, does not require an emission inventory and utilises very low computational resources. It represents an accurate and efficient means of producing real-time air quality forecasts and, when used in combination with forecaster experience, is a useful tool for identifying periods of poor air quality 24 h in advance. The hybrid approach outlined in this paper can easily be applied to produce high-quality forecasts of both NO₂ and additional pollutants at new locations/countries where historical monitoring data are available.     

Simultaneous measurements of dissolved CH<Subscript>4</Subscript> and H<Subscript>2</Subscript> in wetland soils

Biogeochemical processes in wetland soils are complex and are driven by a microbiological community that competes for resources and affects the soil chemistry. Depending on the availability of various electron acceptors, the high carbon input to wetland soils can make them important sources of methane production and emissions. There are two significant pathways for methanogenesis: acetoclastic and hydrogenotrophic methanogenesis. The hydrogenotrophic pathway is dependent on the availability of dissolved hydrogen gas (H₂), and there is significant competition for available H₂. This study presents simultaneous measurements of dissolved methane and H₂ over a 2-year period at three tidal marshes in the New Jersey Meadowlands. Methane reservoirs show a significant correlation with dissolved organic carbon, temperature, and methane emissions, whereas the H₂ concentrations measured with dialysis samplers do not show significant relationships with these field variables. Data presented in this study show that increased dissolved H₂ reservoirs in wetland soils correlate with decreased methane reservoirs, which is consistent with studies that have shown that elevated levels of H₂ inhibit methane production by inhibiting propionate fermentation, resulting in less acetate production and hence decreasing the contribution of acetoclastic methanogenesis to the overall production of methane.     

Vulnerability of Mid-Atlantic Forested Watersheds to Timber Harvest Disturbance

Forested watersheds of the Mid-Atlantic Region are an important economic resource. They are also critical for maintaining water quality, sustaining important ecological services, and providing habitat to many animal and plant species of conservation concern. These forests are vulnerable to disturbance and fragmentation from changing patterns of land use in the Mid-Atlantic Region, and from harvests of commercially mature and relatively inexpensive timber. The U.S. Department of Agriculture Forest Service (USDA-FS) Forest Inventory and Analysis (FIA) compiles data on forest condition by state and county. We have transformed these FIA data to a U.S. Geological Survey (USGS) 6-digithdrologic unit code (HUC 6) watershed base, and projected trends in timber growth, inventory, and harvest to 2025 using a timber economics forecasting model (SRTS). We consider forest sustainability from the perspective of timber production, and from the perspective of landscape stability important to conservation values. Simulation data is combined with FIA planted pine acreage data to form a more complete picture of forest extent, composition, and silvicultural practice. Early recognition of prevailing economic trends which encourage the fragmentation of mature forests due to increasing timber harvests may provide managers and policy makers with a planning tool to mitigate undesirable impacts.     

Ecological footprint accounting: A multi-scale approach based on net primary productivity

The Ecological footprint accounting is a resource accounting tool that is used to track the sustainability of human systems. In this paper, we present a new approach to calculate the Ecological Footprint metrics at different geographic scales using Net Primary Productivity data. Our study focuses on the town of Afourar, Morocco, as a case study examined at three different scales; national, regional, and provincial scale. In contrast with other studies, our footprint accounting results are expressed in what we have called territorial hectares. The accounting results show that geographic scale has a significant influence on the footprint model, where three cases of sustainability were found. This implies that the efforts to maintain the sustainability of territories are more important at some scales than others. We argue that the relationship between sustainability and geographic scale is both strong and complex and that sustainability is a spatially relative concept. Therefore, we conclude that multi-scale analysis is crucial for making sustainable decisions and management policies.     

Measuring forest structure along productivity gradients in the Canadian boreal with small-footprint Lidar

The structure and productivity of boreal forests are key components of the global carbon cycle and impact the resources and habitats available for species. With this research, we characterized the relationship between measurements of forest structure and satellite-derived estimates of gross primary production (GPP) over the Canadian boreal. We acquired stand level indicators of canopy cover, canopy height, and structural complexity from nearly 25,000 km of small-footprint discrete return Light Detection and Ranging (Lidar) data and compared these attributes to GPP estimates derived from the MODerate resolution Imaging Spectroradiometer (MODIS). While limited in our capacity to control for stand age, we removed recently disturbed and managed forests using information on fire history, roads, and anthropogenic change. We found that MODIS GPP was strongly linked to Lidar-derived canopy cover (r?=?0.74, p?<?0.01), however was only weakly related to Lidar-derived canopy height and structural complexity as these attributes are largely a function of stand age. A relationship was apparent between MODIS GPP and the maximum sampled heights derived from Lidar as growth rates and resource availability likely limit tree height in the prolonged absence of disturbance. The most structurally complex stands, as measured by the coefficient of variation of Lidar return heights, occurred where MODIS GPP was highest as productive boreal stands are expected to contain a wider range of tree heights and transition to uneven-aged structures faster than less productive stands. While MODIS GPP related near-linearly to Lidar-derived canopy cover, the weaker relationships to Lidar-derived canopy height and structural complexity highlight the importance of stand age in determining the structure of boreal forests. We conclude that an improved quantification of how both productivity and disturbance shape stand structure is needed to better understand the current state of boreal forests in Canada and how these forests are changing in response to changing climate and disturbance regimes.     

Influence of temperature changes on ambient air NO<Subscript>x</Subscript> chemiluminescence measurements

Users of automatic air pollution monitors are largely unaware of how certain parameters, like temperature, can affect readings. The present work examines the influence of temperature changes on chemiluminescence NO(x) measurements made with a Thermo Scientific 42i analyzer, a model widely used in air monitoring networks and air pollution studies. These changes are grouped into two categories according to European Standard EN 14211: (1) changes in the air surrounding the analyzers and (2) changes in the sampled air. First, the sensitivity tests described in Standard EN 14211 were performed to determine whether the analyzer performance was adapted to the requirements of the standard. The analyzer met the performance criteria of both tests; however, some differences were detected in readings with temperature changes even though the temperature compensator was on. Sample temperature changes were studied more deeply as they were the most critical (they cannot be controlled and differences of several tens of degrees can be present in a single day). Significant differences in readings were obtained when changing sample temperature; however, maximum deviations were around 3% for temperature ranges of 15°C. If other possible uncertainty contributions are controlled and temperature variations with respect to the calibration temperature are not higher than 15°C, the effect of temperature changes could be acceptable and no data correction should have to be applied.     

Evaluating impacts of development and conservation projects using sustainability indicators: Opportunities and challenges

There has been an increased interest in using sustainability indicators for evaluating the impacts of development and conservation projects. Past and recent experiences have shown that sustainability indicators can be powerful tools for measuring the outcomes of various interventions, when used appropriately and adequately. Currently, there is a range of methods for applying sustainability indicators for project impact evaluation at the environment–development interface. At the same time, a number of challenges persist which have implication for impact evaluation processes especially in developing countries. We highlight some key and recurrent challenges, using three cases from Kenya, Indonesia and Brazil.In this study, we have conducted a comparative analysis across multiple projects from the three countries, which aimed to conserve biodiversity and improve livelihoods. The assessments of these projects were designed to evaluate their positive, negative, short-term, long term, direct and indirect impacts. We have identified a set of commonly used sustainability indicators to evaluate the projects and have discussed opportunities and challenges associated with their application. Our analysis shows that impact evaluation processes present good opportunities for applying sustainability indicators. On the other hand, we find that project proponents (e.g. managers, evaluators, donors/funders) face challenges with establishing full impacts of interventions and that these are rooted in monitoring and evaluation processes, lack of evidence-based impacts, difficulties of measuring certain outcomes and concerns over scale of a range of impacts.We outline key lessons learnt from the multiple cases and propose ways to overcome common problems. Results from our analysis demonstrate practical experiences of applying sustainability indicators in developing countries context where there are different prevailing socio-economic, cultural and environmental conditions. The knowledge derived from this study may therefore be useful to a wider range of audience who are concerned with sustainable integration of development and environmental conservation.     

Integrated Assessment Modeling for Global Climate Change: An Infinite Horizon Optimization Viewpoint

In this paper one uses an infinite time horizon optimal control paradigm to deal with three important issues in global climate change integrated assessment, namely the equitable treatment of all the generations involved, the representation of technical progress, and the uncertainty concerning technological progress and climate change processes. The notion of economic sustainability is associated with the concept of turnpike in infinite horizon optimization models. The issue of deciding on the proper discount rate is addressed in this context and a method is proposed to reconcile long term sustainability and short term time preference for current generations. One also formulates a model where environmental damage and/or technical progress are represented as stochastic jump processes. One calls this random evolution a mode switching process. In this context, sustainability is represented by a family of turnpikes, and the economy will be driven from one steady state to the other as the modes switch. These concepts are illustrated on the DICE model and their possible implementation in other types of integrated assessment models is discussed.     

A habitat-based microscale forest classification system for zoning wood production areas to conserve a rare species threatened by logging operations in south-eastern Australia

The production of timber from native forests is presently one of the most controversial land management issues in Australia. Part of this controversy results from the potential impacts of forestry practices on forest-dependent fauna, particularly those that are rare and endangered, such as Leadbeater's Possum Gymnobelideus leadbeateri McCoy, in the forests of central Victoria, south-eastern Australia. A significant proportion of the highly limited distribution of this species overlaps with some of the most valuable wood production forests in Australia within which extensive clearfelling operations are employed to produce timber and pulpwood. These operations can destroy the habitat of G. leadbeateri. The Victoria government agency that is responsible for forest and wildlife management has devised a forest zoning system as part of the management strategies to conserve G. leadbeateri within timber production areas. This is designed to partition the forest into three types of areas: (1) where the conservation of G. leadbeateri is a priority, (2) where wood production is a priority, and, (3) where both land uses are a joint priority. The classification of areas of forest where the conservation of G. leadbeateri is the primary land use is based on an understanding of the habitat requirements of the species. The results of recent field studies, where statistical models of the habitat requirements of G. leadbeateri have been developed and their performance subsequently tested using a new dataset, highlights the need for a new basis to guide the classification of areas for the conservation of the species within wood production forests. We describe a method for devising a forest management zoning system that is based on a statistical model of the habitat requirements of G. leadbeateri and which will better integrate wood production and the conservation of the species. This procedure accounts for the uncertainty in the statistical model and, in turn, reduces the risk that areas where G. leadbeateri occurs are logged, whilst ensuring that other areas are not unnecessarily excluded from timber harvesting.     

Analyzing riparian forest cover changes along the Firniz River in the Mediterranean City of Kahramanmaras in Turkey

Riparian forests adjacent to surface water are important transitional zones which maintain and enrich biodiversity and ensure the sustainability in a forest ecosystem. Also, riparian forests maintain water quality, reduce sediment delivery, enhance habitat areas for aquatic life and wildlife, and provide ecological corridors between the upland and the downstream. However, the riparian ecosystems have been degraded mainly due to human development, forest operations, and agricultural activities. In order to evaluate the impacts of these factors on riparian forests, it is necessary to estimate trends in forest cover changes. This study aims to analyze riparian forest cover changes along the Firniz River located in Mediterranean city of Kahramanmaras in Turkey. Changes in riparian forest cover from 1989 to 2010 have been determined by implementing supervised classification method on a series of Landsat TM imagery of the study area. The results indicated that the classification process applied on 1989 and 2010 images provided overall accuracy of 80.08 and 75 %, respectively. It was found that the most common land use class within the riparian zone was productive forest, followed by degraded forest, agricultural areas, and other land use classes. The results also indicated that the areas of degraded forest and forest openings increased, while productive forest and agricultural areas decreased between the years of 1989 and 2010. The amount of agricultural areas decreased due to the reduction in the population of rural people. According to these results, it can be concluded that special forest management and operation techniques should be implemented to restore the forest ecosystem in riparian areas.     

Relationships of relative humidity with PM<Subscript>2.5</Subscript> and PM<Subscript>10</Subscript> in the Yangtze River Delta,China

Severe particulate matter (PM, including PM_2.5 and PM₁₀) pollution frequently impacts many cities in the Yangtze River Delta (YRD) in China, which has aroused growing concern. In this study, we examined the associations between relative humidity (RH) and PM pollution using the equal step-size statistical method. Our results revealed that RH had an inverted U-shaped relationship with PM_2.5 concentrations (peaking at RH = 45–70%), and an inverted V-shaped relationship (peaking at RH = 40 ± 5%) with PM₁₀, SO₂, and NO₂. The trends of polluted-day number significantly changed at RH = 70%. The very-dry (RH < 45%), dry (RH = 45–60%) and low-humidity (RH = 60–70%) conditions positively affected PM_2.5 and exerted an accumulation effect, while the mid-humidity (RH = 70–80%), high-humidity (RH = 80–90%), and extreme-humidity (RH = 90–100%) conditions played a significant role in reducing particle concentrations. For PM₁₀, the accumulation and reduction effects of RH were split at RH = 45%. Moreover, an upward slope in the PM_2.5/PM₁₀ ratio indicated that the accumulation effects from increasing RH were more intense on PM_2.5 than on PM₁₀, while the opposite was noticed for the reduction effects. Secondary transformations from SO₂ and NO₂ to sulfate and nitrate were mainly responsible for PM_2.5 pollution, and thus, controlling these precursors is effective in mitigating the PM pollution in the YRD, especially during winter. The conclusions in this study will be helpful for regional air-quality management.     

Mega-Development Trends in the Amazon: Implications for Global Change

This paper describes four global-change phenomena that are having major impacts on Amazonian forests. The first is accelerating deforestation and logging. Despite recent government initiatives to slow forest loss, deforestation rates in Brazilian Amazonia have increased from 1.1 million ha yr^–1 in the early 1990s, to nearly 1.5 million ha yr^–1 from 1992–1994, and to more than 1.9 million ha yr^–1 from 1995–1998. Deforestation is also occurring rapidly in some other parts of the Amazon Basin, such as in Bolivia and Ecuador, while industrialized logging is increasing dramatically in the Guianas and central Amazonia.The second phenomenon is that patterns of forest loss and fragmentation are rapidly changing. In recent decades, large-scale deforestation has mainly occurred in the southern and eastern portions of the Amazon — in the Brazilian states of Pará, Maranho, Rondônia, Acre, and Mato Grosso, and in northern Bolivia. While rates of forest loss remain very high in these areas, the development of major new highways is providing direct conduits into the heart of the Amazon. If future trends follow past patterns, land-hungry settlers and loggers may largely bisect the forests of the Amazon Basin.The third phenomenon is that climatic variability is interacting with human land uses, creating additional impacts on forest ecosystems. The 1997/98 El Niño drought, for example, led to a major increase in forest burning, with wildfires raging out of control in the northern Amazonian state of Roraima and other locations. Logging operations, which create labyrinths of roads and tracks in forsts, are increasing fuel loads, desiccation and ignition sources in forest interiors. Forest fragmentation also increases fire susceptibility by creating dry, fire-prone forest edges.Finally, recent evidence suggests that intact Amazonian forests are a globally significant carbon sink, quite possibly caused by higher forest growth rates in response to increasing atmospheric CO₂ fertilization. Evidence for a carbon sink comes from long-term forest mensuration plots, from whole-forest studies of carbon flux and from investigations of atmospheric CO₂ and oxygen isotopes. Unfortunately, intact Amazonian forests are rapidly diminishing. Hence, not only is the destruction of these forests a major source of greenhouse gases, but it is reducing their intrinsic capacity to help buffer the rapid anthropogenic rise in CO₂.     

Disturbance of Dabao highway construction on plant species and soil nutrients in Longitudinal Range Gorge Region (LRGR) of Southwestern China

The disturbance of highway construction upon surrounding vulnerable ecosystems is a common threat in the Longitudinal Range Gorge Region of southwestern China. We evaluated the disturbance of highway on plant species richness and diversity and soil nutrients from adjacent to the highway to 300 m upslope and 100 m downslope in forests and grasslands by setting 12 belt transects in forests and grasslands (six belt transects and six control belt transects, respectively). The results showed that there were some significant variances in belt transects with respective control belt transects for species richness and diversity in both forests and grasslands. Species richness and diversity of trees were lower within a 50-m distance from the highway and more noticeable on the downslope portion. Species richness and diversity of shrubs and herbs appeared higher near highway edge. Both species richness and diversity of herbs were similar in forests. In addition, exotic species, such as Eupatorium adenophorum, were further from the road and more widely dispersed in grasslands. Soil nutrients except total potassium (TK) were lower in the downslope area adjacent to highway edge and showed a significant increase with increasing distance from the highway in both forests and grasslands. This indicates that grasslands acted as microhabitats for exotic species and are more easily to be invaded than forests, especially if disturbed. Once destroyed, plant species and soil nutrients will require a significant amount of time to be restored to control levels. This work illustrates that the effects extend considerably to distances upslope and downslope from the construction site. Given that these changes occurred relatively quickly, the study suggests that the environmental "footprint" grows far beyond the road and adjacent zone of disruption.     

Promoting corporate sustainability through sustainable resource management: A hybrid decision-making approach incorporating social media data

Previous studies revealed triple bottom line cannot entirely cover the concept of corporate sustainability. This study thus uses sustainable resource management (SRM) to improve corporate sustainability (CS) performance considering the socio-environmental, socio-economical, and eco-efficiency aspects. In this study, the vague set (VS) theory and the technique for order preference by similarity to ideal solution (TOPSIS) method are integrated as a hybrid decision-making tool by which social media data can be transformed into entropy weights. The results indicate eco-efficiency and society should be prioritized to improve the corporate sustainability performance. Specifically, the aspects should be promoted through encouraging environmental innovation, redesigning consumers' offer, raising support of the institutions and policy measures, and organizing synergetic involvement. The contributions of this study are three-fold: (i) establishing a comprehensive framework for guiding firms make effective improvements; (ii) developing a hybrid VS-TOPSIS method to process the assessment data and social media information and address the interrelationships; (3) identifying the decisive SRM criteria to precisely guide the Chinese automobile industry towards CS under severe resource constraints.     

Fuel characteristics and emissions from biomass burning and land-use change in Nigeria

Nigeria is one of the 13 low-latitude countries that have significant biomass burning activities. Biomass burning occurs in moist savanna, dry forests, and forest plantations. Fires in the forest zone are associated with slash-and-burn agriculture; the areal extent of burning is estimated to be 80% of the natural savanna. In forest plantations, close to 100% of litter is burned. Current estimates of emissions from land-use change are based on a 1976 national study and extrapolations from it. The following non-carbon dioxide (CO₂) trace gas emissions were calculated from savanna burning: methane (CH₄), 145 gigagrams (Gg); carbon monoxide (CO), 3831 Gg; nitrous oxide (N₂O), 2 Gg; and nitrogen oxides (NO_x), 49 Gg. Deforestation rates in forests and woodlands are 300 × 10³ ha (kilohectare, or kha) and 200 × kha per year, respectively. Trace gas emissions from deforestation were estimated to be 300 Gg CH₄, 2.4 Gg N₂O, and 24 Gg NO_x. CO₂ emissions from burning, decay of biomass, and long-term emissions from soil totaled 125 561 Gg. These estimates should be viewed as preliminary, because greenhouse gas emission inventories from burning, deforestation, and land-use change require two components: fuel load and emission factors. Fuel load is dependent on the areal extent of various land uses, and the biomass stocking and some of these data in Nigeria are highly uncertain.