Suitable environmental areas for commercial timber in the state of Rio de Janeiro,Southeastern Brazil

Environment, Development and Sustainability - In state of Rio de Janeiro, the Southeast Region of Brazil, 97.91% of the reforested areas contain exclusively species of Pinus and Eucalyptus. Due to...     

Arsenic release from pyrite ash waste over an active hydrogeological system and its effects on water quality

Environmental Science and Pollution Research - Arsenic is a potentially toxic element of concern for environmental compartments, and it is a frequent pollutant in many abandoned industrial sites....     

Global and regional potential of wastewater as a water,nutrient and energy source

There is a proactive interest in recovering water, nutrients and energy from waste streams with the increase in municipal wastewater volumes and innovations in resource recovery. Based on the synthesis of wastewater data, this study provides insights into the global and regional “potential” of wastewater as water, nutrient and energy sources while acknowledging the limitations of current resource recovery opportunities and promoting efforts to fast-track high-efficiency returns. The study estimates suggest that, currently, 380 billion m³ (m³ = 1,000 L) of wastewater are produced annually across the world which is a volume five-fold the volume of water passing through Niagara Falls annually. Wastewater production globally is expected to increase by 24% by 2030 and 51% by 2050 over the current level. Among major nutrients, 16.6 Tg (Tg = million metric ton) of nitrogen are embedded in wastewater produced worldwide annually; phosphorus stands at 3.0 Tg and potassium at 6.3 Tg. The full nutrient recovery from wastewater would offset 13.4% of the global demand for these nutrients in agriculture. Beyond nutrient recovery and economic gains, there are critical environmental benefits, such as minimizing eutrophication. At the energy front, the energy embedded in wastewater would be enough to provide electricity to 158 million households. These estimates and projections are based on the maximum theoretical amounts of water, nutrients and energy that exist in the reported municipal wastewater produced worldwide annually. Supporting resource recovery from wastewater will need a step-wise approach to address a range of constraints to deliver a high rate of return in direct support of Sustainable Development Goals (SDG) 6, 7 and 12, but also other Goals, including adaptation to climate change and efforts in advancing “net-zero” energy processes towards a green economy.     

Comparison of the energetic performance of a binary cycle and a flash evaporation binary cycle,using low-enthalpy geothermal energy

In this article, the comparison of the energetic performance of a binary cycle and a modified binary cycle with flash evaporation is presented, both using a low-enthalpy geothermal resource. The comparative analysis is based on two main discourses: the first one evaluates a conventional binary cycle (CBC) with isopentane as working fluid in order to validate and compare the generated data with those reported in similar studies; the second one uses the same input parameters for both cycles, obtaining the cases in which the modified binary cycle is the most viable choice to use. In addition to the above, several aspects are considered when selecting the most suitable working fluid, and the use of water as working fluid is introduced. When the temperature of the geothermal resource is below 140 °C, and the ambient temperature is 25 °C, the flash evaporation binary cycle, using water as working fluid, shows an improved performance compared to a CBC, with a theoretical output over 1000 kWe. This may encourage new areas of opportunity for power generation, not only with low- and medium-enthalpy geothermal energy, but also with other renewable energy sources such as solar.     

A science of integration: frameworks,processes, and products in a place-based,integrative study

Integrative research is increasingly a priority within the scientific community and is a central goal for the evolving field of sustainability science. While it is conceptually attractive, its successful implementation has been challenging and recent work suggests that the move towards interdisciplinarity and transdisciplinarity in sustainability science is being only partially realized. To address this from the perspective of social-ecological systems (SES) research, we examine the process of conducting a science of integration within the Southcentral Alaska Test Case (SCTC) of Alaska-EPSCoR as a test-bed for this approach. The SCTC is part of a large, 5 year, interdisciplinary study investigating changing environments and adaptations to those changes in Alaska. In this paper, we review progress toward a science of integration and present our efforts to confront the practical issues of applying proposed integration frameworks. We: (1) define our integration framework; (2) describe the collaborative processes, including the co-development of science through stakeholder engagement and partnerships; and (3) illustrate potential products of integrative, social-ecological systems research. The approaches we use can also be applied outside of this particular framework. We highlight challenges and propose improvements for integration in sustainability science by addressing the need for common frameworks and improved contextual understanding. These insights may be useful for capacity-building for interdisciplinary projects that address complex real-world social and environmental problems.     

Local Responses to Inundation and De-Farming in the Reservoir Region of the Three Gorges Project (China)

Large-scale infrastructural developments in rural areas often impose significant direct and indirect impacts on environment and people. The Three Gorges Project to dam the Yangtze River in China will create a huge reservoir, inundate farmlands and villages, and incur large-scale resettlement. The concurrent de-farming program to reforest marginal farmlands on steep slopes imposes additional stresses on local people. This study evaluates the ecological and economic adjustments in rural areas affected by both projects, and explores villagers’ knowledge, attitudes, perceptions, and expectations vis-à-vis the drastic changes. Eleven villages in Yunyang County in Sichuan Province, stratified into three zones based on topography and agriculture, were assessed by field studies, questionnaire surveys, maps, satellite imagery, and census and government reports. Multiple regressions identified predictors for 17 dependent variables. Spatial variations in the difficult terrain imposed zone-differentiated agricultural constraints, ecological impacts, and human responses. The dominant farming population—mainly young adults working as migrant laborers in cities—has adopted some nonagricultural work to supplement incomes. Expected per-capita standardized farmland (SF) exceeded threshold SF, which surpasses existing SF. Motivations to reclaim more farmlands, de-farm marginal lands, and become migrant laborers were explained by different multiple-regression predictors. Reduction in farmland stock by inundation and de-farming, aggravated by unwillingness towards nonlocal resettlement, would impose ecological pressures and stimulate demands for nonfarming incomes. Common anticipation of better future income and occupation has been subdued by unfavorable feedbacks from early relocatees. Future environmental and landscape changes are hinged upon changing human responses. Government policies could be informed by research findings to match economic, ecological, and social realities.     

Characterisation of particulate matter on the receptor level in a city environment

Airborne particulate matter (PM) has become one of the dominant pollutants with the increasing material and energy demand due to global economic growth. The main objective of this research is to provide a comprehensive receptor level characterisation of the particulate matter collected in a city environment. Particulate matter samples were collected on Tapered Element Oscillating Microbalance (TEOM) filters from five monitoring sites over a period of 1 year. An Andersen eight-stage cascade impactor was also used to collect airborne PM samples from three other locations to compare with the samples collected by TEOM. All the samples were then subjected to individual particle morphology and chemical composition analysis by SEM/EDS. Bulk chemical composition of the samples were also analysed through ICP–OES. Based on these analyses, possible sources of the PM samples were identified. The results showed that the monitoring sites in residential environments were dominated by transportation-derived particles and other migratory particulates. Monitoring sites near the city centre were dominant by particles from transportation, with biological particles abundant for the site closer to a river. The monitoring station located close to the industrial area, despite only 200 m away from a motorway, has low contribution of non-exhaust particulates from vehicles. Instead, the particulates collected from this site were dominated by industrial sources. An air dispersion modelling package was also used to model the particulate matter dispersion in the city area for the period of sampling. The results from the model showed that the points of high emissions were around industrial areas.     

Evaluation of the temporal scaling variability in forecasting ground-level ozone concentrations obtained from multiple linear regressions

Ozone is a highly unpredictable pollutant which severely affects living conditions in urban and surrounding areas in the Mediterranean basin. This secondary pollutant periodically reaches extremely high concentrations, damaging human health. Multiple linear regression has been widely used in previous works due to the fact that it is a simple and versatile method for forecasting ozone concentrations. However, these models usually prove their validity using fulfillment of statistical constraints, ignoring other intrinsic characteristics existing in the time series, such as the temporal scaling behavior and the data distribution over different time scales. In previous works, it has been demonstrated that observed ozone time series are of a multifractal nature, meaning that the data distribution can be described by using the multifractal spectrum. This work focuses on the capacity of a forecasting model to reproduce the scaling features existing in an observed time series when several chemical and meteorological explanatory variables are introduced following the stepwise procedure. A comparison between the observed spectrum and the simulated ones for each step is used to check which explanatory variables better reproduce the multifractal nature in real ozone time series. It has been confirmed that a model with few explanatory variables allows reproducing the multifractal nature in the simulated time series with an acceptable accuracy without compromising the values of the coefficient of determination and root-mean-squared error, which were used as performance indicators.     

Regional data refine local predictions: modeling the distribution of plant species abundance on a portion of the central plains

Species distribution models are frequently used to predict species occurrences in novel conditions, yet few studies have examined the consequences of extrapolating locally collected data to regional landscapes. Similarly, the process of using regional data to inform local prediction for species distribution models has not been adequately evaluated. Using boosted regression trees, we examined errors associated with extrapolating models developed with locally collected abundance data to regional-scale spatial extents and associated with using regional data for predictions at a local extent for a native and non-native plant species across the northeastern central plains of Colorado. Our objectives were to compare model results and accuracy between those developed locally and extrapolated regionally, those developed regionally and extrapolated locally, and to evaluate extending species distribution modeling from predicting the probability of presence to predicting abundance. We developed models to predict the spatial distribution of plant species abundance using topographic, remotely sensed, land cover and soil taxonomic predictor variables. We compared model predicted mean and range abundance values to observed values between local and regional. We also evaluated model prediction performance based on Pearson's correlation coefficient. We show that: (1) extrapolating local models to regional extents may restrict predictions, (2) regional data can help refine and improve local predictions, and (3) boosted regression trees can be useful to model and predict plant species abundance. Regional sampling designed in concert with large sampling frameworks such as the National Ecological Observatory Network may improve our ability to monitor changes in local species abundance.