soil quality

The soils of arid and semi-arid regions of Iran contain small amount of organic matter due to the lack of sufficient vegetation. Improving the soil organic matter due to the rapid decomposition of the added materials requires the continuous return of organic materials to the soil. Addition of organic amendments such as straw and stubble, manure, sewage sludge and other waste to the soil increases the amount of organic matter in the soil. Sewage sludge is one of the most important organic wastes and byproducts of wastewater treatment processes, and due to the increase in their production in recent decades, its management has become one of the key tasks in the environmental policies of many countries. The use of sewage sludge in agriculture provides a high amount of the nutrients to plants. But potential environmental hazards such as the presence of microbes and heavy metals in sewage sludge is considered as a limiting factor, thus sewage sludge transformation to biochar is a desirable way to manage them. Biochar is a product of thermal decomposition of organic materials in the absence of air (pyrolysis). It has been shown that biochar production from sludge reduce the volume of sludge and removes some heavy metals from the sludge. Biochemical properties of soil are more sensitive to changes in soil management compared to chemical and physical properties. Among them, soil enzymes play an important role in nutrient cycling in nature, additionally they are sensitive indicators to agricultural operations, and respond faster than other soil biological characteristics to changes in soil management and agricultural operations. Therefore, the aim of this study was to investigate the effect of sewage sludge and its biochar on the activities of soil urease, alkaline phosphatase and sucrose enzymes.

A factorial experiment was conducted in a completely randomized design with three replications. Effects of sewage sludge biochar at three level of 0 (B0), 2 (B2) and 4 (B4) percent, the sewage sludge at three level of 0 (S0), 4 (S4) and 8 (S8) precent and four different incubation time were evaluated on soil urease, alkaline phosphatase and sucrase activities. Nine treatments including 1) control; zero level of biochar and sludge (B0S0), 2) 2% w/w of biochar (B2), 3) 4% w/w of biochar (B4), 4) 4% w/w of sludge-sewage (S4) 5) 8% w/w of sewage sludge (S8), 6) 2% w/w of biochar + 4% w/w of sewage sludge (B2S4), 7) 2% w/w of biochar + 8% w/w of sewage sludge (B2S8), 8) 4% w/w Biochar + 4% w/w of sewage sludge (B4S4), 9) 4% w/w of biochar + 8% w/w of sewage sludge (B4S8) was incorporated in the soil. Each of the treatments was mixed with one kilogram of soil samples and then transferred to plastic containers with a capacity of 1.5 kg and their moisture was kept 60-70% of the field capacity. Then the lid of the containers was closed and five holes with a diameter of approximately 2 mm were installed on each for air exchange. During the incubation the moisture of the samples was kept constant by regularly weighing the containers. Sub-samples were taken at intervals of 2, 15, 30 and 60 days. In these samples, pH, organic carbon, total nitrogen, available phosphorus and activities of sucrase (invertase), urease and alkaline phosphatase enzymes were measured.

The activity of urease in various treatments showed a high fluctuation during the incubation time. At the beginning of incubation, the addition of amendments caused a significant increase (p˂0.01) in the activity of this enzyme compared to the control. On day 15, the highest urease activity of urease in was observed in S4 (950 µg NH4+ g-1 h-1) and B2S8 (954 µg NH4+ g-1 h-1). After one month of incubation, the activity of this enzyme in B4, S4 and B4S8 decreased significantly compared to the control, and in other treatments, its amount was significantly higher than that of the control. However, at the end of the incubation time, the maximum activity of this enzyme was achieved in B2S4 (1196 µg NH4+ g-1 h-1). Alkaline phosphatase activity fluctuated highly in different treatments during the incubation period. After one month from the beginning of the experiment, the activity of this enzyme increased significantly in all treatments compared to the control, and the highest amount alkaline phosphates was observed in B2S4 (3348 µg pNP g-1 h-1) and B4S8 (3342 µg pNP g-1 h-1). Nevertheless, by Day 60 the activity of alkaline phosphatase decreased significantly in all treatments compared to the control. Sucrase enzyme activity increased on Day 30 days in all treatments compared to the control. The highest amount of the activity of this enzyme was obtained in S8 at the rate of 2574 µg glucose g-1 h-1. But at the end of the incubation time, the maximum activity of this enzyme was observed in B2S4.

The highest pH was observed in control and B2 treatments. Increasing the dosage of sludge and biochar did not have a significant effect on soil pH, perhaps because the pH of sludge and biochar was lower than soil pH. The highest amount of organic carbon was observed in the B2S8. Increasing the dosage of biochar did not have a significant effect on organic carbon, but increasing the dosage of sludge caused an increase in organic carbon. The highest amount of total N was achieved in B2S8 treatment. Maximum amount of available was observed in B4S8. No significant difference was recorded in higher doses of sludge and biochar, which may be due to stabilization or surface absorption of phosphorus. The highest amount of urease activity was observed in B2S4 and B4S8. In higher doses of biochar, urease activity relatively remained constant, however, higher doses of biochar reduced it. The maximum activity of alkaline phosphatase was achieved in S8, B2S8 and B4S8. The highest amount of sucrase activity was recorded for S8. Overall, our study showed that co-application of sewage sludge and biochar (B2S4) seems suitable to improve the soil urease, alkaline phosphatase and sucrase activity.

Water quality is the process to determine the chemical, physical and biological characteristics of water bodies and identifying the source of any possible pollution or contamination which might cause degradation of the water quality. Due to the rapid growth of industries, the disposal of liquid and solid wastes is increasing, thereby polluting soil and water. If the waste is not disposed of properly, then it percolates into the ground and causes problems like groundwater contamination, degradation of vegetation, soil contamination and modification of soil properties, etc. Nevertheless, traditional methods of water quality monitoring are often expensive and time-consuming. This is especially important for large water bodies such as lakes, dams, and rivers where sampling does not cover the entire body of water. Publicly available RS data are collected at regional scales and temporal resolutions (i.e., repeat collection time) that are much more frequent than field sampling campaigns. The physics and chemical characteristics of water can be determined from spectral signatures. Also, extracting water quality measurements directly from satellite imagery can allow rapid identification of impaired waters, potentially leading to faster responses by water agencies. Remote sensing data is an appropriate alternative to monitoring water resources due to its time and cost-effectiveness in a wide range of temporal and spatial scales. Currently, there are various types of remote sensing data such as hyperspectral and multispectral data that can be used to monitor and evaluate water quality. Geographical information systems (GIS) and remote sensing (RS) have been used extensively to assess the water quality all over the world. The Euphrates River is one of the most important rivers in Iraq, which has hosted various civilizations in the ancient Mesopotamia region since ancient times and is still of great importance to the urban and rural communities of Iraq. The Hillah River is one of the two main branches of the Euphrates River, which flows eastward by branching off from it. This river is the most important river in the Babylon governorate in Iraq, which passes through a wide area and several small streams flow from it to supply water to agricultural lands in other governorates. The Hillah River passes through several cities and is affected by industrial, agricultural, and domestic wastewater, which has received less attention than other areas of the Euphrates River. For this purpose, in this research, a detailed assessment of the quality and pollution of the Hillah River in the Babylon governorate is carried out using different methods of remote sensing, GIS, and field and laboratory operations to determine the quality of this river.the purpose of its performance is to assessment the quality of water and soil for the area of Hillah river in Babylon governorate in Iraq. The method of collecting data and information needed to perform quantitative and qualitative analyzes in research was based on field, laboratory and library operations, and various software tools were used in data processing. In order to determine and collect water and soil quality samples, field operations have been used. For this purpose, the area of Hillah city is considered as the base point and samples have been collected parallel to the river Hillah in the north and south of the city. Accordingly, in terms of number, distribution and accuracy in field sampling, 10 points were collected from the area by using Garmin handheld GPS device, 7 points were taken from water and 3 points were taken from the soil of the area. The field work to determine the sampling locations was based on several reconnaissance trips and as a result, the locations of the main water sampling stations were identified. Then, they visited the desired places twice a month, and each time they visited, relevant samples were taken. The samples were collected in standard plastic bottles with a capacity of 1.5 liters and their lids were tightly closed. Paying attention to the change in composition, soil samples were taken with a wider spatial distribution and from places with far distances from each other in the Hillah river basin, and the volume of each soil sample varied between 1 and 1.5 kg. Two different laboratories in Babylon governorate have been referred to perform quality tests on the collected samples. The laboratory measures have been carried out in two separate stages. In the first step, the measures of preparing the samples and separating them from each other have been carried out, which includes labeling, determining the date of water and soil samples, and classifying the samples for laboratory analysis. In the second stage, laboratory equipment and operations have been used for the qualitative analysis of the samples, and various devices such as CRISON have been used to test the physical and chemical parameters on the samples. Using laboratory tools and facilities, various physical and chemical variables of water quality have been measured based on the collected samples. For this purpose, 13 parameters have been tested on the samples. Electrical conductivity and total dissolved solids were measured using an EC meter and pH using a pH meter according to the relevant methods. The capacity of calcium and magnesium ions in water samples has been measured using the weighing method. Soluble sulfate, phosphate and nitrate were measured by a spectrophotometer. Sodium concentration in water was measured by flame photometer. Chloride in water was estimated from the scaling method using silver nitrate standard solution and using potassium chromate solution as the relevant guide and the results were expressed in ppm. Total hardness was measured as calcium and magnesium in water as milligrams per liter or ppm. Turbidimeteror is used to measure water turbidity. Finally, the iodometric method has been used to measure dissolved oxygen in water. In soil quality measurement, in addition to the parameters of electrical conductivity, pH, calcium, magnesium and sodium, which are also evaluated in the measurement of water samples, other parameters were also measured. including sodium absorption ratio (SAR) and calcium carbonate (CaCo). For the measurement of the mentioned two elements, special laboratory tools have been used like other elements. WQI index has been used to evaluate the water quality at the region of the Hillah river. For this purpose, the data related to the stations sampled from the water level were entered into the calculations of the WQI index, and based on this index, the water quality was evaluated on a monthly basis, and water quality maps were prepared for the region. The WQI index equation creates a range between 1 and 100, where 1 means the poorest and 100 the best water quality, and within this range, five classes are set to classify the water quality as very poor or inadequate, poor, moderately good, good and excellent. For satellite images processing, Landsat satellite imagery data provided by the United States Geological Survey (USGS) database archive has been used. In this regard, the image of Landsat 8 satellite OLI sensor for the date 2021/06/27 of the area has been selected as the main satellite data for processing.The research results can be presented in several sections. In the analysis of water quality in terms of quality parameters, it has been determined that, except for several cases in different months, in most cases, the concentration of chemical parameters of water did not exceed the permissible limit, and the physical parameters were appropriate. However, the results of the drinking water quality index have shown that the water of the Hillah River is at a poor and very poor level in terms of quality according to the location of the samples, and the spatial quality map of the Hillah river has also shown that the central to northern areas are of a more suitable quality than The southern regions have it, the main reason of which is the concentration of agricultural lands and the entry of waste and various effluents into the water in those areas. The results of evaluating the physical and chemical quality of soil in the studied area have also shown that soils with sandy texture are richer than mixed clay soils in terms of parameters such as electrical conductivity, sodium, calcium, magnesium, and SAR, and on the other hand, pH and calcium carbonate of Clay soils were more than sandy soils. The evaluation of the correlation of the parameters between the values of the water and soil samples has been done and the coefficient of the orrelation between them has been obtained, and in some cases, there has been a high correlation between the parameters. Finally, by evaluating the correlation between the quality parameters and the Landsat image bands in terms of combinations and band ratios, it has been determined that there was a direct correlation in a few cases, and on the other hand, the linear relationship also indicated the absence of a relationship between the WQI index and the spectral bands.