Thorium uptake by wheat at different stages of plant growth

Data on biogeochemistry of thorium are rather limited. So far little is known about toxic effects of small amounts of the radionuclide on higher plants. In this study the uptake of thorium by wheat seedlings was measured by greenhouse experiments. Germination of wheat seeds for 6 days in the presence of thorium resulted in accumulation of the metal in all parts of the seedlings. When the Th-rich seedlings were transferred to normal soil and were grown there further for 7 days, Th concentrations in roots and leaves decreased significantly (in leaves the Th content decreased up to the level of Th in the control plants). In seeds, however, Th content remained unchanged. An increase of Th content in roots and seeds was also observed as a result of addition of thorium to soil but in this case the concentration of Th in leaves did not change. The accumulation of Th in plants affected the uptake of other elements including essential macro-nutrients. The most strongly affected part of the plants was leaf.     

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.     

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.     

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.     

Does green environmental innovation really matter for carbon-free economy? Nexus among green technological innovation,green international trade,and green power generation

Environmental Science and Pollution Research - This study examines the association among the green energy production (GEP), green technological innovation (GTI), and green international trade (GIT)...     

The relationship between staple food crops consumption and its impact on total factor productivity: does green economy matter?

The agriculture sector is a key driver of economic growth and provides employment opportunities across the globe generally. However, in today’s world, agricultural product demand is more influenced by taste, prices, and nutritional value due to climatic variation. The study has analyzed the current situation grain productivity by using the data of farm inputs and major grain crops of Pakistan from (1960–2020). The study consists of a two-stage analysis in the first stage, the total factor productivity (TFP) variable is obtained by using the parametric Tornqvisit-Theil index output-input-aggregation method separately for each crop; rice, maize, and wheat. After that, the unit root test is used to check the stationarity and trend of the variables in the long run. Subsequently, the autoregressive distributed lag (ARDL) model is applied to check the existence of cointegration in the long run and short run among the variables. The results of the study disclosed that the consumption of rice has a positive relationship with its total factor productivity, but, wheat and maize have a negative long-run cointegration relationship with the respective productivities. The study results have shown that the consumption pattern of staple crops has substantially changed, due to climatic variation, and the current food consumption trend is revealing new dimensions and trends owing to variation in climate change and anthropogenic pressure which demands to adapt climate resilient farm practices.