FLOOD FORECASTING FOR TUCURUI HYDROELECTRICAL PLANT,BRAZIL1

ABSTRACT: The construction of the Tucurui Hydroelectric Plant on the Tocantins River basin in Brazil requires flood forecasting for ensuring the safety of the cofferdam. The latter has been initially designed for a flood with a return frequency of one in 25 years. Lack of adequate forecasting facilities during the earlier stages of construction has resulted in significant damages and construction delays. Statistical forecasting models were developed by Projeto de Hidrologiay Climatologie da Amazonia (PHCA) for the purpose of preventing further damages at the site. These models are currently in use and are the subject of this paper. The application of these models during the 1980 flood season, when the highest flood on record occurred at the Tucurui site, proved of great assistance in preventing the flooding of the cofferdam. In conjunction with the development of these models a number of data collection platforms using data transmission through the GOES system were installed to provide automatically the data required for forecasting.     

Air pollution perception in ten countries during the COVID-19 pandemic

As largely documented in the literature, the stark restrictions enforced worldwide in 2020 to curb the COVID-19 pandemic also curtailed the production of air pollutants to some extent. This study investigates the perception of the air pollution as assessed by individuals located in ten countries: Australia, Brazil, China, Ghana, India, Iran, Italy, Norway, South Africa and the USA. The perceptions towards air quality were evaluated by employing an online survey administered in May 2020. Participants (N = 9394) in the ten countries expressed their opinions according to a Likert-scale response. A reduction in pollutant concentration was clearly perceived, albeit to a different extent, by all populations. The survey participants located in India and Italy perceived the largest drop in the air pollution concentration; conversely, the smallest variation was perceived among Chinese and Norwegian respondents. Among all the demographic indicators considered, only gender proved to be statistically significant.Supplementary InformationThe online version contains supplementary material available at 10.1007/s13280-021-01574-2.     

Livelihood strategies,environmental dependency and rural poverty: the case of two villages in rural Mozambique

This article attempts to explore the nexus between rural households’ environmental dependency, poverty and livelihood strategies. Households’ income from each livelihood activities formed the basis for categorizing households according to livelihood strategies. The principal component analysis, agglomerative hierarchical and the k-means cluster analysis were employed to determine the four livelihood clusters and to assign households to the identified livelihood strategies. Households’ environmental dependency, poverty and asset holding were compared across the strategies, and the determinants of livelihood choice were analyzed using multinomial logit model. The results indicate the existence of marked differences in environmental dependency, rural poverty and asset endowments across the livelihood groups. Household’s total saving, access to credit, production implements, business cost, exposure to agricultural shock determined household’s access to a more remunerative livelihood strategy. Incomes from each livelihood activities for the identified livelihood strategies were analyzed, and their implications were also discussed.     

Nutrient reduction in runoff water from sugarcane farms by sedimentation method

Due to intensive use of agronomic inputs in sugarcane farming, runoff water from these farms is loaded with high concentrations of nutrients. These nutrients find their way into rivers, lakes and sinks, eutrophicating them. Reducing the levels of these nutrients in runoff water from sugarcane farms before it is discharged into sinks will help solve the problems that arise out of eutrophication. This study employed a simple sedimentation method of making depressions in canals draining runoff water from sugarcane farms and emptying them fortnightly during the rainy season and monthly during the dry season. The method was found to significantly (p????0.05) reduce water conductivity (??S/cm), turbidity (Nephelometric Turbidity Units), total phosphates, nitrate?Cnitrogen, potassium, calcium, magnesium, iron, copper, sodium and zinc (ppm) in the dry season from 52.89, 148.70,0.87, 3.34, 446.00, 420.00, 205.00, 12,941.00, 261.00, 398.00, and 484.00 in untreated canals to 48.33, 30.22, 0.21, 2.95, 120.00, 154.00, 98.00, 456.00, 181.00, 234.00, and 311.00 in treated canals, respectively. And in the wet season, the parameters were reduced from 261.46, 719.30, 820.00, 25.16, 654.00, 549.00, 493.00, 19,230.00, 763.00, 748.00, and 903.00 to 128.67, 365.70, 3.47, 10.12, 136.00, 187.00, 167.00, 654.00, 207.00, 321.00, and 231.00, respectively. Dissolved oxygen significantly (p????0.05) increased from 5.11 to 8.14?ppm in the dry season and from 3.82 to 7.92?ppm wet season. Acidity reduced in the wet season from pH 5.02 to 6.20. It is, therefore, recommended that sugarcane farmers adopt this method for sustainability of aquatic systems within these zones.     

Roadside vehicle particulate matter concentration estimation using artificial neural network model in Addis Ababa, Ethiopia

Currently, vehicle-related particulate matter is the main determinant air pollution in the urban environment. This study was designed to investigate the level of fine (PM_2.5) and coarse particle (PM₁₀) concentration of roadside vehicles in Addis Ababa, the capital city of Ethiopia using artificial neural network model. To train, test and validate the model, the traffic volume, weather data and particulate matter concentrations were collected from 15 different sites in the city. The experimental results showed that the city average 24-hr PM_2.5 concentration is 13%–144% and 58%–241% higher than air quality index (AQI) and world health organization (WHO) standards, respectively. The PM₁₀ results also exceeded the AQI (54%–65%) and WHO (8%–395%) standards. The model runs using the Levenberg-Marquardt (Trainlm) and the Scaled Conjugate Gradient (Trainscg) and comparison were performed, to identify the minimum fractional error between the observed and the predicted value. The two models were determined using the correlation coefficient and other statistical parameters. The Trainscg model, the average concentration of PM_2.5 and PM₁₀ exhaust emission correlation coefficient were predicted to be (R² = 0.775) and (R² = 0.92), respectively. The Trainlm model has also well predicted the exhaust emission of PM_2.5 (R² = 0.943) and PM₁₀ (R² = 0.959). The overall results showed that a better correlation coefficient obtained in the Trainlm model, could be considered as optional methods to predict transport-related particulate matter concentration emission using traffic volume and weather data for Ethiopia cities and other countries that have similar geographical and development settings.     

An investigation into the anthropogenic nexus among consumption of energy,tourism, and economic growth: do economic policy uncertainties matter?

Environmental Science and Pollution Research - Global warming has been a pressing issue for the past decade as various economic activities have been flagged and are expected to reduce emissions....     

Terrestrial, benthic, and pelagic resource use in lakes: results from a three-isotope Bayesian mixing model

Fluxes of organic matter across habitat boundaries are common in food webs. These fluxes may strongly influence community dynamics, depending on the extent to which they are used by consumers. Yet understanding of basal resource use by consumers is limited, because describing trophic pathways in complex food webs is difficult. We quantified resource use for zooplankton, zoobenthos, and fishes in four low-productivity lakes, using a Bayesian mixing model and measurements of hydrogen, carbon, and nitrogen stable isotope ratios. Multiple sources of uncertainty were explicitly incorporated into the model. As a result, posterior estimates of resource use were often broad distributions; nevertheless, clear patterns were evident. Zooplankton relied on terrestrial and pelagic primary production, while zoobenthos and fishes relied on terrestrial and benthic primary production. Across all consumer groups terrestrial reliance tended to be higher, and benthic reliance lower, in lakes where light penetration was low due to inputs of terrestrial dissolved organic carbon. These results support and refine an emerging consensus that terrestrial and benthic support of lake food webs can be substantial, and they imply that changes in the relative availability of basal resources drive the strength of cross-habitat trophic connections.     

A global community-sourced assessment of the state of conservation technology

Conservation technology holds the potential to vastly increase conservationists’ ability to understand and address critical environmental challenges, but systemic constraints appear to hamper its development and adoption. Understanding of these constraints and opportunities for advancement remains limited. We conducted a global online survey of 248 conservation technology users and developers to identify perceptions of existing tools’ current performance and potential impact, user and developer constraints, and key opportunities for growth. We also conducted focus groups with 45 leading experts to triangulate findings. The technologies with the highest perceived potential were machine learning and computer vision, eDNA and genomics, and networked sensors. A total of 95%, 94%, and 92% respondents, respectively, rated them as very helpful or game changers. The most pressing challenges affecting the field as a whole were competition for limited funding, duplication of efforts, and inadequate capacity building. A total of 76%, 67%, and 55% respondents, respectively, identified these as primary concerns. The key opportunities for growth identified in focus groups were increasing collaboration and information sharing, improving the interoperability of tools, and enhancing capacity for data analyses at scale. Some constraints appeared to disproportionately affect marginalized groups. Respondents in countries with developing economies were more likely to report being constrained by upfront costs, maintenance costs, and development funding (p = 0.048, odds ratio [OR] = 2.78; p = 0.005, OR = 4.23; p = 0.024, OR = 4.26), and female respondents were more likely to report being constrained by development funding and perceived technical skills (p = 0.027, OR = 3.98; p = 0.048, OR = 2.33). To our knowledge, this is the first attempt to formally capture the perspectives and needs of the global conservation technology community, providing foundational data that can serve as a benchmark to measure progress. We see tremendous potential for this community to further the vision they define, in which collaboration trumps competition; solutions are open, accessible, and interoperable; and user-friendly processing tools empower the rapid translation of data into conservation action. Article impact statement: Addressing financing, coordination, and capacity-building constraints is critical to the development and adoption of conservation technology.