Pollution area identification, receptor model-oriented sources and probabilistic health hazards to prioritize control measures for heavy metal management in soil.

Identifying the primary source of heavy metals (HMs) pollution and the key pollutants is crucial for safeguarding eco-health and managing risks in industrial vicinity. For this purpose, this investigation was carried out to investigate the pollution area identification with soil static environmental capacity (QI), receptor model-oriented critical sources, and Monte Carlo simulation (MCS) based probabilistic environmental and human health hazards associated with HMs in agricultural soils of Narayanganj, Bangladesh. The average concentration of Cr, Ni, Cu, Cd, Pb, Co, Zn, and Mn were 98.67, 63.41, 37.39, 1.28, 23.93, 14.48, 125.08, and 467.45 mg/kg, respectively. The geoaccumulation index identified Cd as the dominant metal, indicating heavy to extreme contamination in soils. The QI revealed that over 99% of the areas were polluted for Ni and Cd with less uncertain regions whereas Cr showed a significant portion of areas with uncertain pollution status. The positive matrix factorization (PMF) model identified three major sources: agricultural (29%), vehicular emissions (25%), and industrial (46%). The probabilistic assessment of health hazards indicated that both carcinogenic and non-carcinogenic risks for adult male, adult female, and children were deemed unacceptable. Moreover, children faced a higher health hazard compared to adults. For adult male, adult female, and children, industrial operations contributed 48.4%, 42.7%, and 71.2% of the carcinogenic risks, respectively and these risks were associated with Ni and Cr as the main pollutants of concern. The study emphasizes valuable scientific insights for environmental managers to tackle soil pollution from HMs by effectively managing anthropogenic sources. It could aid in devising strategies for environmental remediation engineering and refining industry standards.

Combined Effect of Salicylic Acid and Proline Mitigates Drought Stress in Rice (Oryza sativa L.) through the Modulation of Physiological Attributes and Antioxidant Enzymes.

Salicylic acid (SA) and proline exhibit protective effects against a wide range of stresses. However, the combined impact of SA and proline on rice under drought stress is still unknown. Therefore, we investigated the protective roles of SA and/or proline in conferring drought tolerance in rice. There were eight treatments comprising the control (T1; 95-100% FC), 1.5 mM SA (T2), 2 mM proline (T3), 0.75 mM SA + 1 mM proline (T4), 45-50% FC (T5, drought stress), T5 + 1.5 mM SA (T6), T5 + 2 mM proline (T7), and T5 + 0.75 mM SA + 1 mM proline (T8), and two rice varieties: BRRI dhan66 and BRRI dhan75. Drought stress significantly decreased the plant growth, biomass, yield attributes, photosynthetic rate (Pn), stomatal conductance (gs), transpiration rate (Tr), photosynthetic pigments (chlorophyll and carotenoids content), relative water content (RWC), membrane stability index (MSI), soluble sugar and starch content, and uptake of N, P and K+ in roots and shoots. Drought-induced oxidative stress in the form of increased hydrogen peroxide (H2O2) production and lipid peroxidation (MDA) was observed. The combined application of SA (0.75 mM) + proline (1 mM) was found to be more effective than the single application of either for drought stress mitigation in rice. A combined dose of SA + proline alleviated oxidative stress through boosting antioxidant enzymatic activity in contrast to their separate application. The application of SA + proline also enhanced proline, soluble sugar and starch content, which resulted in the amelioration of osmotic stress. Consequently, the combined application of SA and proline significantly increased the gas exchange characteristics, photosynthetic pigments, RWC, MSI, nutrient uptake, plant growth, biomass and yield of rice. Therefore, the combined application of SA and proline alleviated the detrimental impacts of drought stress more pronouncedly than their separate application did by increasing osmoprotectants, improving nutrient transport, up-regulating antioxidant enzyme activity and inhibiting oxidative stress.

Agricultural livelihoods in coastal Bangladesh under climate and environmental change--a model framework.

Coastal Bangladesh experiences significant poverty and hazards today and is highly vulnerable to climate and environmental change over the coming decades. Coastal stakeholders are demanding information to assist in the decision making processes, including simulation models to explore how different interventions, under different plausible future socio-economic and environmental scenarios, could alleviate environmental risks and promote development. Many existing simulation models neglect the complex interdependencies between the socio-economic and environmental system of coastal Bangladesh. Here an integrated approach has been proposed to develop a simulation model to support agriculture and poverty-based analysis and decision-making in coastal Bangladesh. In particular, we show how a simulation model of farmer's livelihoods at the household level can be achieved. An extended version of the FAO's CROPWAT agriculture model has been integrated with a downscaled regional demography model to simulate net agriculture profit. This is used together with a household income-expenses balance and a loans logical tree to simulate the evolution of food security indicators and poverty levels. Modelling identifies salinity and temperature stress as limiting factors to crop productivity and fertilisation due to atmospheric carbon dioxide concentrations as a reinforcing factor. The crop simulation results compare well with expected outcomes but also reveal some unexpected behaviours. For example, under current model assumptions, temperature is more important than salinity for crop production. The agriculture-based livelihood and poverty simulations highlight the critical significance of debt through informal and formal loans set at such levels as to persistently undermine the well-being of agriculture-dependent households. Simulations also indicate that progressive approaches to agriculture (i.e. diversification) might not provide the clear economic benefit from the perspective of pricing due to greater susceptibility to climate vagaries. The livelihood and poverty results highlight the importance of the holistic consideration of the human-nature system and the careful selection of poverty indicators. Although the simulation model at this stage contains the minimum elements required to simulate the complexity of farmer livelihood interactions in coastal Bangladesh, the crop and socio-economic findings compare well with expected behaviours. The presented integrated model is the first step to develop a holistic, transferable analytic method and tool for coastal Bangladesh.