Climate change/variability implications on hydroelectricity generation in the Zambezi River Basin

The study has analysed the effects of various factors on hydroelectric power generation potential to include climate change/variability, water demand, and installation of proposed hydroelectric power schemes in the Zambezi River Basin. An assessment of historical (1970–2000) power potential in relation to climate change/variability at existing hydro electric power schemes(Cahora Bassa, Kariba, Kafue Gorge and Itezhi-Tezhi) in the Zambezi River Basin was conducted. The correlation of hydroelectric power potential with climate change/variability aimed at observing the link and extent of influence of the latter on the former was investigated. In order to predict the future outlook of hydro electric power potential, General Circulation Models (GCM) were used to generate projected precipitation. The monthly simulated precipitation was extracted from the GCM for every sub basin and used to compute future precipitation. Further, future water demand in the sub basins of the Zambezi River Basin were estimated based on the respective population growth rate in each sub basin. Subsequently, water balance model, with projected precipitation and water demand input was used to determine projected run-offs of sub basins of the Zambezi River Basin. .Based on the projected run-offs of sub basins, reservoir storage capacities at existing hydro electric power schemes were estimated. The baseline assessment revealed a strong relationship between hydroelectric power potential and climate change/variability. The study also revealed that the main climate and other risks associated with current and future hydro electric power generation include projected dry years, floods and increasing water demand. The results indicate that the hydroelectric power potential has a tendency towards gradual reduction in its potential in all existing and proposed hydroelectric power schemes owing to climate change and increasing water demand.     

A note on the thyroid function of the pregnant females in the USA: data from NHANES 1999–2002 and 2007–2012

Thyroid dysfunction during pregnancy has been shown to be associated with adverse pregnancy outcomes. Consequently, this study was undertaken to assess the thyroid health of the pregnant females in the United States. Publically available data from National Health and Nutrition Examination Survey for the cycles 1999–2002 and 2007–2012 were analyzed for this purpose. Over 22% (SE: 5.7%) of the pregnant females based on the unweighted data in their first trimester had higher than the recommended levels of thyroid-stimulating hormone (TSH) or 2.5 mIU/L. Free triiodothyronine (FT3) and free thyroxine (FT4) levels decreased over pregnancy trimesters. Based on unweighted data, about 6% (SE: 2.6%) pregnant females were positive for thyroglobulin antibodies and 34.9% (SE: 5.2%) pregnant females were positive for thyroid peroxidase antibodies. Over 55% (SE: 3.5%) pregnant females based on the unweighted data were iodine deficient. As many as 41% (SE: 3%) first trimester pregnant females having TSH > 2.5 mIU/L is of concern. Future research should concentrate on developing educational and other materials that can advise pregnant females during their prenatal care and other appointments about the adverse effects of high TSH levels and what they can do to keep their TSH levels within the recommended levels.     

Hydrologic evaluation of landfill performance (HELP) modeling in bioreactor landfill design and permitting

The practice of operating municipal solid waste landfills as bioreactor landfills has become more common over the past decade. Because simulating moisture balance and flow is more critical in such landfills than in dry landfills, researchers have developed methods to address this problem using the hydrologic evaluation of landfill performance (HELP) model. This paper discusses three methods of applying the HELP model to simulate the percolation of liquids added to landfill waste: the leachate recirculation feature (LRF), the subsurface inflow (SSI) feature, and additional rainfall to mimic liquids addition. The LRF is simple to use but may not be able to bring the landfill to bioreactor conditions. The SSI feature provides a convenient user interface for modeling liquids addition to each layer. The additional rainfall feature provides flexibility to the model, allowing users to estimate the leachate generation rate and the leachate head on bottom liner associated with daily variation in the liquids addition rate. Additionally, this paper discusses several issues that may affect the HELP model, such as the time of model simulation, layers of liquids addition, and the limitations of the HELP model itself. Based on the simulation results, it is suggested that the HELP model should be run over an extended period of time after the cessation of liquids addition in order to capture the peak leachate generation rate and the head on the liner (HOL). From the perspectives of leachate generation and the HOL, there are few differences between single-layer injection and multiple-layer injection. This paper also discusses the limitations of using the HELP model for designing and permitting bioreactor landfills.     

Connecting science,policy, and implementation for landscape‐scale habitat connectivity

We examined the links between the science and policy of habitat corridors to better understand how corridors can be implemented effectively. As a case study, we focused on a suite of landscape‐scale connectivity plans in tropical and subtropical Asia (Malaysia, Singapore, and Bhutan). The process of corridor designation may be more efficient if the scientific determination of optimal corridor locations and arrangement is synchronized in time with political buy‐in and establishment of policies to create corridors. Land tenure and the intactness of existing habitat in the region are also important to consider because optimal connectivity strategies may be very different if there are few, versus many, political jurisdictions (including commercial and traditional land tenures) and intact versus degraded habitat between patches. Novel financing mechanisms for corridors include bed taxes, payments for ecosystem services, and strategic forest certifications. Gaps in knowledge of effective corridor design include an understanding of how corridors, particularly those managed by local communities, can be protected from degradation and unsustainable hunting. There is a critical need for quantitative, data‐driven models that can be used to prioritize potential corridors or multicorridor networks based on their relative contributions to long‐term metacommunity persistence.     

Population and development: impacts on environmental performance

Population growth and development patterns have a significant impact on the environmental performance. The issue of concern is whether population growth or the consumption/production patterns are responsible for environmental deterioration. This paper is an attempt to capture the impact of technological development, affluence, and population on environmental performance index, while previous studies had captured the impact of these three factors on environment only through CO₂ emissions. The analysis reveals that technological development and population size have a negative impact on environmental performance, whereas measures to improve affluence have a positive impact. Technological development has increased the production of energy efficient products but at the same time consumption of these products has increased manifold leading to environmental deterioration. Demographic attributes need specific attention to improve environmental performance. This paper concludes on some policy reflections on slowing the population growth as well as persuades individuals and economies to relook to their consumption and production patterns and channelize their efforts to protect the environment.     

Assessment of deterioration in water quality from source to household storage in semi-urban settings of developing countries

This study has investigated the common risks associated with the water quality changes from the source to the consumer households and the associated disease burden in the piped water supplies. Samples from the source to the household storage from Nagpur City were collected and analysed for heavy metals, nutrient and microbial parameters. Sanitary risks were identified at the households during the socio-economic and sanitary survey. The water quality deterioration was the most at household storage around 30.3 % indicating that measures need to be taken to safeguard the water quality at the consumer level. Then, 31.2 % of the samples collected from public standposts and handpumps were positive for faecal contamination which implies that it is the weaker sections of the society who suffer the consequences of drinking unsafe water the most. On the basis of the laboratory results, risk analysis by surveying the WTPs, point-of-use behaviour at households and sanitary status at different socio-economic strata, the Water Safety Plan for Nagpur City was structured. The aim was to ensure that safe and improved water is reached to the individual household.     

A Wavelet-based Neural Network Model to Predict Ambient Air Pollutants’ Concentration

The present paper proposes a wavelet based recurrent neural network model to forecast one step ahead hourly, daily mean and daily maximum concentrations of ambient CO, NO₂, NO, O₃, SO₂ and PM_2.5 — the most prevalent air pollutants in urban atmosphere. The time series of each air pollutant has been decomposed into different time-scale components using maximum overlap wavelet transform (MODWT). These time-scale components were made to pass through Elman network. The number of nodes in the network was decided on the basis of the strength (power) of the corresponding input signals. The wavelet network model was then used to obtain one-step ahead forecasts for a period extending from January 2009 to June 2010. The model results for out of sample forecast are reasonably good in terms of model performance parameters such as mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), normalized mean absolute error (NMSE), index of agreement (IOA) and standard average error (SAE). The MAPE values for daily maximum concentrations of CO, NO₂, NO, O₃, SO₂ and PM2.5 were found to be 9.5%, 17.37%, 21.20%, 13.79%, 17.77% and 11.94%, respectively, at ITO, Delhi, India. Bearing in mind that the forecasts are for daily maximum concentrations tested over a long validation period, the forecast performance of the model may be considered as reasonably good. The model results demonstrate that a judicious selection of wavelet network design may be employed successfully for air quality forecasting.     

The role of policy for the management of psychosocial risks at the workplace in the European Union

A number of significant developments towards the management of psychosocial risks have been achieved at the policy level in the European Union (EU) since the introduction of the 1989 European Commission Council Framework Directive 89/391/EEC on Safety and Health of Workers at Work on which a new EU risk prevention culture has since been established, combining legislation, social dialogue, best practices and building partnerships. However, it has been widely acknowledged that initiatives aiming to promote workers’ health have not had the impact anticipated both by experts and policy makers and the main reason for this has been the gap that exists between policy and practice. This paper discusses the findings from the PRIMA-EF project, a policy-orientated project, which focussed on the development of a European framework for psychosocial risk management at the workplace. In particular, the paper presents the results of an EU stakeholder survey and interviews with EU policy level experts to assess their awareness, understanding and evaluation of the impact of policy initiatives for psychosocial risk management.     

Vulnerability of Indian mustard (<Emphasis Type="Italic">Brassica juncea</Emphasis> (L.) Czernj. Cosson) to climate variability and future adaptation strategies

A simulation study has been carried out using the InfoCrop mustard model to assess the impact of climate change and adaptation gains and to delineate the vulnerable regions for mustard (Brassica juncea (L.) Czernj. Cosson) production in India. On an all India basis, climate change is projected to reduce mustard grain yield by ～2 % in 2020 (2010–2039), ～7.9 % in 2050 (2040–2069) and ～15 % in 2080 (2070–2099) climate scenarios of MIROC3.2.HI (a global climate model) and Providing Regional Climates for Impact Studies (PRECIS, a regional climate model) models, if no adaptation is followed. However, spatiotemporal variations exist for the magnitude of impacts. Yield is projected to reduce in regions with current mean seasonal temperature regimes above 25/10 °C during crop growth. Adapting to climate change through a combination of improved input efficiency, additional fertilizers and adjusting the sowing time of current varieties can increase yield by ～17 %. With improved varieties, yield can be enhanced by ～25 % in 2020 climate scenario. But, projected benefits may reduce thereafter. Development of short-duration varieties and improved crop husbandry becomes essential for sustaining mustard yield in future climates. As climatically suitable period for mustard cultivation may reduce in future, short-duration (<130 days) cultivars with 63 % pod filling period will become more adaptable. There is a need to look beyond the suggested adaptation strategy to minimize the yield reduction in net vulnerable regions.