Optimizing Textile Dyeing Wastewater Irrigation through Physiochemical Attributes of Tomato, Plant Nutrient Use Efficiency and Pollution Load Index of Irrigated Soil

This experiment considers the seven different stages of textile dyeing effluents on tomato crop production in order to diminish the excess effluent treatment plant (ETP) cost and farmers net input cost. Seven different stages waste water (WW) with ground water (control) were collected and analyzed for physiochemical as well as heavy metals properties. T8 (mixed effluent) crossed the limit of agricultural standard for almost all physiological parameters such as TDS, TSS, EC, BOD, COD affording the highest value. T8 also delivered the highest cl- and heavy metals like Cd, Ni, Cr followed by T4 (2nd wash after bath drain) < T7 (Fixing treatment water). As a consequence, these provided comparatively higher enrichment factor (EF), pollution load index (PLI) and sodium absorption ratio (SAR) to transform fresh soil into “severe” and “slightly to moderate” saline. Correlation matrix demonstrated that EF and PLI of heavy metals (except Cd, Ni) were negatively related to yield, while positively related to SAR and fruit abortion. Although T6 (2nd wash after soaping) performed better in respect to growth, yield, yield attributes and nutrient use efficiency, principal component analysis (PCA) expressed that T2 (2nd wash after scouring and bleaching) and T3 (enzyme treated water) also belong to T6 and T1 group (ground water). Therefore, T2, T3 and T6 could be used to vegetable crop production up to some extent and to reduce ETP and agricultural input cost.

Comparative Water Environment Simulation Study of Two Typical Models with BMPs in a Karst Basin

Carbonate rocks are widely distributed in southwest China, forming a unique karst landscape. The Lijiang River Basin provides a typical example of an area with concentrated karst. Research on the laws of hydrology and water quality migration in the Lijiang River Basin is important for the management of the water resources of Guilin City and similar areas. In this study, we combined three meteorological data with the soil and water assessment tool (SWAT) model and the hydrological simulation program-Fortran (HSPF) model to simulate the hydrological and water quality processes in the Lijiang River Basin separately. We chose the Nash–Sutcliffe efficiency (NSE) coefficient, coefficient of determination (R2), root mean square error-observations standard deviation ratio (RSR), and mean absolute error (MAE) as the metrics used to evaluate the models. The results, combined with the time-series process lines, indicated that the SWAT model provides a more accurate performance than the HSPF model in streamflow, ammonia nitrogen (NH3-N), and dissolved oxygen (DO) simulations. In addition, we divided the karst and non-karst areas, and we analyzed the differences between them in water balance, sediment transport, and pollution load. We further identified the key source areas of pollution load in the Lijiang River Basin, evaluated the pollution reduction effect of best management practices (BMPs) on surface source pollution, and proposed some pollution control countermeasures. Each scenario, especially returning farmland to forest and creating vegetation buffer zones, reduces the NH3-N and DO pollution load.

Study of Some Indicators of Environmental Pollution of Surface Soil for the City of Touggourt (Southeast Algeria)

Soil is contaminated with various potentially harmful metals (PTMs). Therefore, the adequate protection of soil from contamination is imperative, as the soil is regarded as the primary cradle for living and environmental balance. Accordingly, the purpose of this study was to assess the contamination level by PTMs in Touggourt city, where soil samples have been collected randomly from 18 sites. These sites included manufacturing companies and institutions belonging to the industrial region of Touggourt city. The concentrations of six PTMs - zinc (Zn), iron (Fe), cobalt (Co), copper (Cu), lead (Pb) and manganese (Mn) were assessed using the atomic absorption spectrophotometer (AAS) instrument as well as the application of the modern pollution indices such as CF (Contamination Factor), PLI (Pollution Load Index) and EF (Enrichment Factor). The highest values of contamination factor (CF) for Zn, Fe, Co, Cu, and Pb were 0.605, 1.605, 0.277, 0.05, 0.438, and 0.01, respectively, and the highest value of pollution load index (PLI) was 0.139, while the results of enrichment factor (EF) for the Zn, Mn, Co, Cu and Pb metals were 2.608, 0.060, 0.740, 0.122, and 2.358, respectively. According to these pollution indices, the results of this study have indicated that human effects or industrial wastes and traffic, in particular, were the sources of heavy metal contaminating the studied region.

Sub-Watershed Parameter Transplantation Method for Non-Point Source Pollution Estimation in Complex Underlying Surface Environment

The prevention and control of non-point source pollution is an important link in managing basin water quality and is an important factor governing the environmental protection of watershed water in China over the next few decades. The control of non-point source pollution relies on the recognition of the amount, location, and influencing factors. The watershed nonpoint source pollution mechanism model is an effective method to address the issue. However, due to the complexity and randomness of non-point source pollution, both the development and application of the watershed water environment model have always focused on the accuracy and rationality of model parameters. In this pursuit, the present study envisaged the temporal and spatial heterogeneity of non-point source pollution caused by the complex underlying surface conditions of the watershed, and the insufficient coverage of hydrological and water quality monitoring stations. A refined watershed non-point source pollution simulation method, combining the Monte Carlo analytic hierarchy process (MCAHP) and the sub-watershed parameter transplantation method (SWPT), was established on the basis of the migration and transformation theory of the non-point source pollution, considering the index selection, watershed division, sub-watershed simulation, and parameter migration. Taking the Erhai Lake, a typical plateau lake in China, as the representative research object, the MCAHP method effectively reduced the uncertainty of the weights of the watershed division indexes compared to the traditional AHP method. Furthermore, compared to the traditional all watershed parameter simulation (AWPS) approach, the simulation accuracy was improved by 40% using the SWPT method, which is important for the prevention and control of non-point source pollution in large-scale watersheds with significant differences in climatic and topographic conditions. Based on the simulation results, the key factors affecting the load of the non-point source pollution in the Erhai watershed were identified. The results showed that the agricultural land in Erhai Lake contributed a majority of the load for several reasons, including the application of nitro phosphor complex fertilizer. Among the different soil types, paddy soil was responsible for the largest pollution load of total nitrogen and total phosphorus discharge into the lake. The zones with slopes of 0°‒18° were found to be the appropriate area for farming. Our study presents technical methods for the assessment, prevention, and control of non-point source pollution load in complex watersheds.

Different Approaches to Assessing Pollution Load: The Case of Nitrogen-Related Grey Water Footprint of Barley and Soybean in Argentina

Agriculture is among the main causes of water pollution. Currently, 75% of global anthropogenic nitrogen (N) loads come from leaching/runoff from cropland. The grey water footprint (GWF) is an indicator of water resource pollution, which allows for the evaluation and monitoring of pollutant loads (L) that can affect water. However, in the literature, there are different approaches to estimating L and thus contrasting GWF estimates: (A1) leaching/runoff fraction approach, (A2) surplus approach and (A3) soil nitrogen balance approach. This study compares these approaches for the first time to assess which one is best adapted to real crop production conditions and optimises GWF calculation. The three approaches are applied to assess N-related GWF in barley and soybean. For barley in 2019, A3 estimated a GWF value 285 to 196% higher than A1, while in 2020, the A3 estimate was 135 to 81% higher. Soybean did not produce a GWF due to the crop characteristics. A3 incorporated N partitioning within the agroecosystem and considered different N inputs beyond fertilization, improving the accuracy of L and GWF estimation. Providing robust GWF results to decision-makers may help to prevent or reduce the impacts of activities that threaten the world’s water ecosystems and supply.