Located in a semi-arid and arid zone, Iran is suffering from growing challenges of water scarcity. In the paradigm of a circular economy, the reuse of treated wastewater in agriculture is currently regarded as a possible solution for alleviating the issues of water scarcity and pollution. Accordingly, this research aims to assess the use of polluted water in the integrated management of water resources in Semnan. The research used the Water Evaluation and Planning System (WEAP) software package to model and analyze the water sector. Also, the Bayesian network method was used to assess the risk of using polluted water and its effects on humans and plants. The research explored two general scenarios for the study site of Semnan. The first scenario assumes the increase in population, crops (food), and industries, and the second has the same assumptions plus an increase in agricultural efficiency (food production). Based on the results, the agricultural, urban, and industrial water demands are 37, 0.06, and 0.01 million m3 in the base year, respectively. The water demand in the next years will be higher due to population growth. Finally, it is safer to use the wastewater of both treatment plants of the region (Mehdishahr and Semnan) in the industry than in other sectors. Additionally, the wastewater of the Mehdishahr Sewage Treatment Plant is more reliable than that of the Semnan Sewage Treatment Plant.

  • Management of a smart city.

  • Critical assessment on Iran's water resources development.

  • A novel framework and multisectoral approach toward the implementation of IWRM and the W&F nexus in Iran, case study Semnan, is proposed.

  • Enhancing IWRM and W&F nexus may eradicate hunger as the agriculture sector is disconnected with water, by replacing water reuse.

Graphical Abstract

Graphical Abstract
Graphical Abstract

Several decades ago water was considered an abundant natural resource because it was renewable in all seasons (Santos Pereira et al. 2009). However, the planet Earth is currently suffering from the growing scarcity of water (Aliabadi et al. 2020; Ofori et al. 2021), especially in arid and semi-arid regions whose limited water resources are depleting (Aliabadi et al. 2022; Es'haghi et al. 2022). According to FAO (2014), water, food, and energy are interrelated and at risk due to population growth, urbanization, economic and industrial development, and climate change. So, the concept of the water-energy-food nexus has emerged to describe their complicated interactions to allow sustainable use of these limited resources. Agriculture is, indeed, the biggest freshwater consumer (Singh 2021), accounting for 69 percent of global water use. However, this figure is as high as 92 percent in Iran. The annual water use of Iran amounts to 96 billion m3, out of which 52 billion m3 is supplied by groundwater tables, 42 billion m3 by surface water resources, and the remaining by water freshening and recycling (Ataei et al. 2019; Karimi & Ataei 2022).

Thus, water scarcity for agriculture can have important implications for food security and nourishment (Akbari et al. 2022). In addition, the existing water resources seem to be inadequate to meet the growing water demands, which will create a gap in water demand and supply (Nan et al. 2020; Ataei et al. 2021). Furthermore, pollution caused by human activities reduces water quality and renders it inappropriate for most purposes (Santos Pereira et al. 2009).

To cope with this situation, the reuse of treated wastewater as an unconventional water resource has recently gained much significance (Salgot 2018). Today, wastewater is considered a renewable and cheap unconventional resource, not a source of pollution (Almuktar et al. 2018; Chojnacka et al. 2020; Rizzo et al. 2020). Therefore, in regions where there is water scarcity, wastewater can be used to supplement or replace fresh water for applications in which freshwater quality is not important, especially in agriculture and industry (Helmecke et al. 2020; Ofori et al. 2021). Treated wastewater reuse provides an opportunity to address the gap between water demand and supply in Iran since wastewater can meet a significant part of total water demand if treated and recycled properly.

The history of wastewater reuse can provide a field for planning projects and adopting new policies on wastewater reuse. The evolution of wastewater treatment technology and its diverse applications have recently become more important incentives for wastewater reuse projects. Domestic wastewater has historically been used for irrigation by various civilizations including the civilizations of China, Egypt, and Mesopotamia (Angelakis et al. 2018). The first registered ‘wastewater farm’ was reported in Bunzlau, Poland in 1531. Farms in Edinburgh, Scotland were irrigated with urban wastewater in 1650 (Crites et al. 2021). Wastewater is broadly defined as the ‘used’ water that has been polluted by human activities (Mateo-Sagasta et al. 2015). It can be raw (gray or black water) or diluted and can potentially be very harmful to human health and the environment, but provided that the standards are observed, it is increasingly regarded as a reliable and economic source of water, particularly for agricultural or industrial applications (Qadir et al. 2010; Mizyed & Mays 2020).

In addition to reducing the burden on freshwater resources, treated wastewater reuse in agriculture has economic and environmental advantages, including the supply of nutrients (N and P) and organic matter. As such, it helps increase agriculture productivity besides reducing chemical fertilization and the relevant costs and preserving the quality of freshwater resources by reducing wastewater discharge into water bodies (Chojnacka et al. 2020). Although there are numerous benefits of wastewater reuse, it can have public health hazards if not managed soundly (Jaramillo & Restrepo 2017; Chojnacka et al. 2020; Rizzo et al. 2020). Wastewater reuse, indeed, entails extensive disadvantages regarding undesirable pollutants, e.g., organic matter with high levels of chemical oxygen demand (COD) and biochemical oxygen demand (BOD), total suspended solids (TSS), nutrients (e.g., N and P), heavy metals (e.g., cadmium, chromium, nickel, lead, copper, and zinc), emerging pollutants (e.g., organic solvents, pesticides, and medications), toxic anions, and pathogens (bacteria, viruses, protozoa, and nematodes) (Kaushal et al. 2018). These pollutants may adversely affect soil, groundwater quality, and human health (Jaramillo & Restrepo 2017; Chojnacka et al. 2020). To tackle these pitfalls, wastewater must be subjected to proper treatment as per the relevant standards and regulations to be safe for reuse in the industry, agriculture, and urban sector.

Data on the current levels of produced, available, and reused wastewater quantities at different scales are dispersed so that they are rarely monitored and reported and are unavailable in most countries (Mateo-Sagasta et al. 2015; Janeiro et al. 2020). Furthermore, treatment can lead to very different levels of quality, and treatment plants may operate at capacities (much) lower than the capacities reported (Oliveira & Von Sperling 2008; Murray & Drechsel 2011). Also, data on on-site wastewater treatment are often missing although on-site systems may represent most populations. Even in high-income countries, a significant number of people rely on on-site systems such as septic tanks and are not connected to urban wastewater systems. For example, one out of five American households relies on small community cluster systems (septic systems) (Rocha Cuadros 2016).

Crites et al. (2021) state that most technological advancements that support the development of modern projects of agricultural water reuse improve the possibility of different competing applications of urban water reuse. The consequences of increasing water shortage for existing water quality and quantity influence agricultural water reuse projects. The historical trend of the development of concentrated and regional facilities of wastewater treatment in the vicinity of proper surface water discharge sites may be amended to better consider agricultural irrigation in integrated water resources planning.

Since Iran (especially the study site in Semnan province) is geographically located in an arid and semi-arid climate and considering the phenomenon of climate change in recent years, water resource management in the 20–25-year horizon is of high significance. Government officials should consider useful solutions for the future by presenting different scenarios based on the potential of the resources in the region. Population growth increases water use in the drinking and industrial sectors and escalates the demand for food production, resulting in greater water use for crop production. Almost half of the water consumed in different sectors, including drinking, agricultural, and industrial sectors, is returned to the environment as wastewater. The treatment of this wastewater and its reuse as contaminated water as per environmental considerations would allow a significant part of water consumption to be reduced. To resolve the deficiency of one of the most critical resources (water) in Semnan province, measures should be taken for the sound management of water resources. The present research aims to use solutions that are compatible with the potential capacities of the study site in Semnan. Although the study site is close to the capital city of Tehran and rainy regions in the north of Iran, the research has ignored such solutions as inter-basin water transfer and has tried to rely on the potential capabilities of the region.

It should be noted that polluted water in this research refers to the wastewater treated in the treatment plants within the study site of Semnan. There are three treatment plants in this region, including the treatment plants of Shahmirzad, Mehdishahr, and Semnan with capacities of 2,400, 6,000, and 15,000 m3/day, respectively. (Since the Shahmirzad treatment plant is at the pilot exploitation phase and has no outlet wastewater, it was not included in the analysis.) A measure considered in this research is the use of contaminated water to solve the problem of water deficiency in the coming years assuming that properly treated contaminated waters can be used as a key resource to meet the water demand of the agricultural and industrial sectors. This research attempts to explore the potential capacities of the study site for water consumption and to estimate the need for these resources in the study site.

Background

The study site is Semnan province, one of the sub-basins of the central Kavir basin, which is located in the center of Iran with an area of 2,150.9 km2 whose plains, uplands, and aquifer have areas of 746.8, 1,404.1, and 614.6 km2, respectively (Figure 1). Four important cities of the study site include Shahmirzad, Mehdishahr, Darjazin, and Semnan from the upstream of the basin to the downstream, respectively. The uplands of the Semnan region have a mean annual temperature of 11.7 °C, whereas its plain has a mean annual temperature of 15.6 °C, an annual evaporation rate of 1,861 mm, mean annual precipitation of 193 mm, a minimum elevation of 797 m, and a maximum elevation of 3,260 m. There are two rivers within the study site – the Golrudbar river with a mean annual flow rate of 0.231 m3/s and the Hajiabad river with a mean annual flow rate of 0.075 m3/s. There are three groundwater resources in the region, including wells, springs, and Qanats whose maximum discharge rates are 57.78, 34.56, and 12.93 million m3, respectively. Among the wells, 160, 27, and 136 are used for agricultural, drinking water, and industrial and service uses, respectively. Semnan's total annual water consumption amounts to 124.7 million m3 – 70.55 percent by the agricultural sector, 25.92 percent to meet the demand for drinking water, and the remaining for the industrial sector.
Figure 1

The study site (Semnan Region, Semnan Province, Iran).

Figure 1

The study site (Semnan Region, Semnan Province, Iran).

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Data sources

The relevant organizations and agencies, including the Department of Meteorology, Regional Water Company, and Water and Wastewater Company of Semnan province, have collected data on surface and groundwater flows, which represent the water resources existing in the region, and the amount of water consumption by the drinking water, industrial, and agricultural sectors.

Study approach

The research used the Water Evaluation and Planning System (WEAP) software package to model and analyze the water sector. Initially, the water resources budget should be explored in the study site so that the input of the regional water resources and all demands, including the demands for drinking water, industrial, and agricultural uses, are determined as per the data available for several consecutive years. So, a conceptual model of the water resources is developed for the base year and then the needs of the demand points are estimated for the next 20–25 years based on different factors, such as climate change and population growth. The methodology framework is presented in Figure 2.
Figure 2

Methodology framework.

Figure 2

Methodology framework.

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In the next step, proper approaches should be adopted to meet the needs of the demand points on the horizon of water resources management. A method proposed and scrutinized in this research is the evaluation of contaminated water reuse. However, it is first necessary to quantify the pollutants in contaminated waters produced by the drinking resources in the study site. Then, the wastewater produced from these resources should be treated as per the environmental requirements and studies. Knowing the needs of different demand points and the amount of contaminated water used to satisfy these needs, different management scenarios are presented and studied.

The use of polluted water in the agricultural and industrial sectors can have various benefits, e.g., the alleviation of water limitations and the reduction of environmental pollution. But, the fact that there may be dangerous pollutants in polluted water limits its application. So, it is imperative to evaluate the risk of using polluted water as an efficient way of recognizing the possible hazards and their potential implications. The present research has used the Bayesian network to evaluate the risk of using polluted water and its impact on humans and plants as receptor factors.

The research investigated two general scenarios for the study site of Semnan. The first scenario assumes increases in population, crop production, and industries, and the second scenario assumes the first scenario's assumptions plus an increase in agriculture efficiency. Finally, the risk of polluted water consumption in the industrial and agricultural sectors is assessed.

Scenario 1&2 (connection/difference)

In Scenario 1, it is aimed to study the available water resources per population growth in the future years. Population growth in the coming years will increase water consumption. By food or crop increase in this scenario, we mean that since the food consumed within the study site is partially produced there, crop production in the study site must be increased to meet food demand given the population growth and consequently, the increase in food demand. Then, in addition to studying the population growth and the increase in crop production, the scenario addresses increasing industrial production in the study site with a focus on increasing industrial towns considering regional potential. Again, the issue of water demand for increasing industrial production is raised, and since the region is facing water scarcity, attention is drawn to the use of wastewater as the input water of the industries.

By wastewater, we mean the sewage treated by the treatment plants within the study site, including the treatment plants of Mehdishahr and Semanan. Their output can be consumed in the industrial and agricultural sectors.

Scenario 2 assumes an increase in agricultural efficiency in addition to population growth, the increase in industrial production, and the increase in crop production. The increase in agricultural efficiency contributes to reducing water consumption while producing more crops. In this scenario, it is aimed to study the effect of using modern irrigation methods (drip and sprinkler irrigation systems) at farms and orchards, which will enhance efficiency in the agricultural sector.

Model developments

To model water and food resources, regional characteristics are first studied. Example characteristics are the geographical location, the physical features of the basin, the hydrological features of the basin including rivers and hydrometer stations, meteorological conditions, and climatology including precipitation, evapotranspiration, the status of groundwater resources, and basin water uses such as its use for drinking water, industry, and agriculture. Also, information such as the area of agricultural lands, the crops cultivated, and the crops harvested are studied to check the food production resources in the region.

The research has considered the aquatic year 2018–2019 (from September 2018 to August 2019) as the current accounts. To achieve the data required for running the water resources management scenarios, the precipitation rate was first calculated for the next 25 years by using the time series method with 35-year data of rainfall in the two basins of the Darjazin and Hajiabad rivers.

Data generation

Calculation of monthly supply and annual demand

A site's demand is defined as the sum of the demands of all its downstream branches (Sieber et al. 2005).
formula
(1)
in which Br is the sub-branch, DS is the site's demand, Total Activity Level is the activity level of each branch (population, cultivated area, and industrial production), and Water Use Rate is the rate of use including per capita drinking water use, water use per unit of cultivated area, and water use of the industrial sector. The activity level of each branch is the sum of the activity levels of its sub-branches (Sieber et al. 2005).

Monthly demand

The monthly demand of a demand site is calculated as the percent of its monthly demand per annual demand. In other words,
formula
(2)
in which m represents the number of months.

Monthly supply requirement

Monthly demand shows the amount of water required per month for site use whereas the supply requirement is the real need made from a supply source. The supply requirement is affected by internal reuse, the management strategy for delivery to the demand site, and the decline in demand (Yates et al. 2005)
formula
(3)
in which Monthly Supply Requirement is used in m3, Reuse Rate is the rate of reusable water (%), DSMSaving is the flow reserved in each demand site, and Loss Rate is the amount of waste in each demand site (%).

Water inflow and outflow

This step provides us with the amount of water inflow and outflow of each node and loop in the system (Yates et al. 2005).
formula
(4)
in ΔS is the reservoir variations.

Agricultural water demand was calculated based on such parameters as the cultivation area, crop type, and irrigation method with the NETWAT software package, and the information required on agricultural needs was inputted into the WEAP software package.

Water demand (drinking, agricultural, industrial)

The demand for drinking water was estimated by considering the per capita drinking water consumption for population centers (urban and rural) within the study site and their populations. The water demand of the industrial sector was also estimated per annum by WEAP according to the information and data received to estimate the annual need of the industry. The agricultural, drinking, and industrial water needs were 37, 0.06, and 0.01 million m3 for the current accounts, respectively.

Table 1 and Figure 3 present the total water demand in the two scenarios for the years 2018–2044. It is observed that future water demand increases with population growth and development. The population is growing in both scenarios. Given the existing potential, the population growth rate is 2% for Semnan and 1.5% for the other cities within the study site including Mehdishahr, Shahmirzad, and Darjazin. Given the fact that population growth naturally entails an increase in the demand for crops and industrial products, the increase in crop and industrial production and the resulting increase in the use of water resources were considered in modeling the water resources with WEAP.
Table 1

The total, satisfied, and unsatisfied water demands (million m3)

YearScenario 1
Scenario 2
Total demandSatisfied demandUnsatisfied demandTotal demandSatisfied demandUnsatisfied demand
2018 37.44 37.44 0.00 37.44 37.44 0.00 
2019 40.27 35.09 5.18 39.18 38.99 0.19 
2020 40.67 40.58 0.09 39.58 39.58 0.00 
2021 41.08 38.35 2.73 39.97 39.72 0.26 
2022 41.49 39.14 2.34 40.37 40.37 0.00 
2023 41.90 40.30 1.60 40.78 40.61 0.16 
2024 42.32 41.92 0.41 41.19 41.19 0.00 
2025 42.75 39.82 2.92 41.60 41.29 0.31 
2026 43.18 36.32 6.86 42.01 41.17 0.84 
2027 43.61 42.44 1.17 42.44 42.39 0.05 
2028 44.04 43.03 1.02 42.86 42.86 0.00 
2029 44.49 40.84 3.64 43.29 43.11 0.18 
2030 44.93 34.48 10.46 43.72 42.02 1.71 
2031 45.38 43.49 1.90 44.16 43.97 0.19 
2032 45.84 42.60 3.23 44.60 44.20 0.40 
2033 46.29 43.21 3.08 45.05 45.05 0.00 
2034 46.76 44.81 1.95 45.50 45.25 0.25 
2035 47.23 46.68 0.54 45.96 45.96 0.00 
2036 47.70 44.25 3.45 46.42 45.96 0.46 
2037 48.18 40.14 8.04 46.88 45.63 1.25 
2038 48.66 47.13 1.53 47.35 47.24 0.11 
2039 49.15 48.08 1.07 47.83 47.80 0.03 
2040 49.64 48.22 1.42 48.30 48.30 0.00 
2041 50.14 45.56 4.58 48.79 48.46 0.33 
2042 50.64 50.27 0.37 49.28 49.28 0.00 
2043 51.15 50.00 1.14 49.77 49.72 0.05 
2044 51.66 50.10 1.56 50.27 50.27 0.00 
YearScenario 1
Scenario 2
Total demandSatisfied demandUnsatisfied demandTotal demandSatisfied demandUnsatisfied demand
2018 37.44 37.44 0.00 37.44 37.44 0.00 
2019 40.27 35.09 5.18 39.18 38.99 0.19 
2020 40.67 40.58 0.09 39.58 39.58 0.00 
2021 41.08 38.35 2.73 39.97 39.72 0.26 
2022 41.49 39.14 2.34 40.37 40.37 0.00 
2023 41.90 40.30 1.60 40.78 40.61 0.16 
2024 42.32 41.92 0.41 41.19 41.19 0.00 
2025 42.75 39.82 2.92 41.60 41.29 0.31 
2026 43.18 36.32 6.86 42.01 41.17 0.84 
2027 43.61 42.44 1.17 42.44 42.39 0.05 
2028 44.04 43.03 1.02 42.86 42.86 0.00 
2029 44.49 40.84 3.64 43.29 43.11 0.18 
2030 44.93 34.48 10.46 43.72 42.02 1.71 
2031 45.38 43.49 1.90 44.16 43.97 0.19 
2032 45.84 42.60 3.23 44.60 44.20 0.40 
2033 46.29 43.21 3.08 45.05 45.05 0.00 
2034 46.76 44.81 1.95 45.50 45.25 0.25 
2035 47.23 46.68 0.54 45.96 45.96 0.00 
2036 47.70 44.25 3.45 46.42 45.96 0.46 
2037 48.18 40.14 8.04 46.88 45.63 1.25 
2038 48.66 47.13 1.53 47.35 47.24 0.11 
2039 49.15 48.08 1.07 47.83 47.80 0.03 
2040 49.64 48.22 1.42 48.30 48.30 0.00 
2041 50.14 45.56 4.58 48.79 48.46 0.33 
2042 50.64 50.27 0.37 49.28 49.28 0.00 
2043 51.15 50.00 1.14 49.77 49.72 0.05 
2044 51.66 50.10 1.56 50.27 50.27 0.00 
Figure 3

The total, satisfied, and unsatisfied water demands (million m3).

Figure 3

The total, satisfied, and unsatisfied water demands (million m3).

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As was already mentioned, the two scenarios differ in agricultural efficiency. In other words, it is attempted to enhance agricultural efficiency in Scenario 2, which is achieved by modifying the cropping pattern and/or implementing modern irrigation schemes. Water consumption can be optimized by changing the cropping pattern and using crops whose water requirements match the water potential of the study site. Furthermore, equipping agricultural lands with modern irrigation systems, e.g., sprinkler and drip irrigation systems, can contribute to saving agricultural water use and preventing water loss.

The year 2018, which was considered the base year, represents a normal year whose water demands are met by the water potential of the region. Given the long-term statistical period of the region, the input of the water resources in the future years along with rainy and dry years was estimated by the time-series method. For example, the unmet demand of the basin would reach about 10 million m3 in 2030 given the regional potential, showing that the year 2030 will be a dry year or the precipitation will be deficient in previous years.

Since the water demand is not satisfied, it is recommended to use polluted water to supply agricultural and industrial water requirements using the risk assessment of these water resources by the Bayesian network.

Table 2 presents the water requirements of the agricultural, drinking water, and industrial sectors over 2019–2044. According to this table, the water requirement will increase in the coming years significantly. As has been illustrated in Figure 4, among these water demand points, agriculture accounts for the greatest water requirement in the study site of Semnan. The agricultural, drinking water, and industrial demands account for 99.8, 0.18, and 0.03 percent of the total water demand of the region, respectively.
Table 2

The water requirement for the three demand groups (m3)

YearTotal demand
AgricultureUrbanIndustry
2018 37,368,230 62,129.12 11,814.45 
2019 40,191,576 63,309.47 11,873.52 
2020 40,593,492 64,512.5 11,932.89 
2021 40,999,427 65,738.65 11,992.55 
2022 41,409,421 66,988.35 12,052.52 
2023 41,823,516 68,262.08 12,112.78 
2024 42,241,751 69,560.29 12,173.34 
2025 42,664,168 70,883.45 12,234.21 
2026 43,090,810 72,232.06 12,295.38 
2027 43,521,718 73,606.61 12,356.86 
2028 43,956,935 75,007.59 12,418.64 
2029 44,396,505 76,435.53 12,480.74 
2030 44,840,470 77,890.94 12,543.14 
2031 45,288,874 79,374.36 12,605.86 
2032 45,741,763 80,886.34 12,668.88 
2033 46,199,181 82,427.42 12,732.23 
2034 46,661,172 83,998.17 12,795.89 
2035 47,127,784 85,599.17 12,859.87 
2036 47,599,062 87,231.01 12,924.17 
2037 48,075,053 88,894.28 12,988.79 
2038 48,555,803 90,589.6 13,053.73 
2039 49,041,361 92,317.58 13,119 
2040 49,531,775 94,078.87 13,184.6 
2041 50,027,093 95,874.11 13,250.52 
2042 50,527,363 97,703.95 13,316.77 
2043 51,032,637 99,569.08 13,383.36 
2044 51,542,964 101,470.2 13,450.27 
YearTotal demand
AgricultureUrbanIndustry
2018 37,368,230 62,129.12 11,814.45 
2019 40,191,576 63,309.47 11,873.52 
2020 40,593,492 64,512.5 11,932.89 
2021 40,999,427 65,738.65 11,992.55 
2022 41,409,421 66,988.35 12,052.52 
2023 41,823,516 68,262.08 12,112.78 
2024 42,241,751 69,560.29 12,173.34 
2025 42,664,168 70,883.45 12,234.21 
2026 43,090,810 72,232.06 12,295.38 
2027 43,521,718 73,606.61 12,356.86 
2028 43,956,935 75,007.59 12,418.64 
2029 44,396,505 76,435.53 12,480.74 
2030 44,840,470 77,890.94 12,543.14 
2031 45,288,874 79,374.36 12,605.86 
2032 45,741,763 80,886.34 12,668.88 
2033 46,199,181 82,427.42 12,732.23 
2034 46,661,172 83,998.17 12,795.89 
2035 47,127,784 85,599.17 12,859.87 
2036 47,599,062 87,231.01 12,924.17 
2037 48,075,053 88,894.28 12,988.79 
2038 48,555,803 90,589.6 13,053.73 
2039 49,041,361 92,317.58 13,119 
2040 49,531,775 94,078.87 13,184.6 
2041 50,027,093 95,874.11 13,250.52 
2042 50,527,363 97,703.95 13,316.77 
2043 51,032,637 99,569.08 13,383.36 
2044 51,542,964 101,470.2 13,450.27 
Figure 4

The percentage of water requirement of different demand points in the study site of Semnan.

Figure 4

The percentage of water requirement of different demand points in the study site of Semnan.

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Water demand (satisfied/unsatisfied) in Scenario 1

Table 3 tabulates the results of Scenario 1. This scenario investigates the water consumption of the agricultural, drinking water, and industrial sectors within the study site of Semnan over 2019–2044. In this scenario, all water demand is satisfied using the potential resources of the region, which mostly include groundwater tables. In this scenario, the population in the population centers of the region is increasing from the base year (2018) to the horizon year (2044), so the increase in crop (food) production and the expansion of the industry are also considered. Finally, the results of Scenario 1 in Table 3 present the water requirements of the agricultural, drinking water, and industrial sectors. The table also shows the satisfied and unsatisfied water demands in this scenario for the studied years. It is observed that the demand for drinking water is fully satisfied in all studied years.

Table 3

The amount of satisfied and unsatisfied water demands in Scenario 1 (m3)

YearSatisfied
Unsatisfied
AgricultureUrbanIndustryAgricultureUrbanIndustry
2018 37,368,230 62,129.12 11,814.45 
2019 35,016,672 63,309.47 11,592.05 5,174,904 281.4765 
2020 40,502,913 64,512.5 11,903.96 90,578.7 28.93128 
2021 38,272,203 65,738.65 11,907.2 2,727,224 85.35913 
2022 39,064,767 66,988.35 11,971.65 2,344,654 80.86266 
2023 40,220,104 68,262.08 12,053.58 1,603,412 59.19735 
2024 41,834,538 69,560.29 12,144.48 407,213 28.86003 
2025 39,740,167 70,883.45 12,176.15 2,924,002 58.05765 
2026 36,231,481 72,232.06 12,123.2 6,859,329 172.1859 
2027 42,351,577 73,606.61 12,267.86 1,170,141 88.99798 
2028 42,937,717 75,007.59 12,361.27 1,019,218 57.36771 
2029 40,753,817 76,435.53 12,359.13 3,642,688 121.6068 
2030 34,385,659 77,890.94 12,305.39 10,454,811 237.7475 
2031 43,393,787 79,374.36 12,453.17 1,895,088 152.686 
2032 42,508,928 80,886.34 12,582.4 3,232,835 86.47943 
2033 43,116,788 82,427.42 12,652.91 3,082,393 79.32086 
2034 44,713,780 83,998.17 12,733.62 1,947,392 62.26526 
2035 46,585,367 85,599.17 12,798.58 542,416.8 61.28557 
2036 44,148,877 87,231.01 12,839.87 3,450,185 84.29419 
2037 40,034,722 88,894.28 12,804.76 8,040,331 184.0345 
2038 47,027,500 90,589.6 12,964.36 1,528,303 89.37007 
2039 47,975,261 92,317.58 13,026.81 1,066,101 92.19454 
2040 48,116,107 94,078.87 13,099.47 1,415,668 85.1295 
2041 45,450,399 95,874.11 13,128.81 4,576,694 121.709 
2042 50,161,998 97,703.95 13,284.44 365,365.8 32.33329 
2043 49,887,794 99,569.08 13,290.71 1,144,843 92.64186 
2044 49,984,378 101,470.2 13,362.83 1,558,585 87.44587 
YearSatisfied
Unsatisfied
AgricultureUrbanIndustryAgricultureUrbanIndustry
2018 37,368,230 62,129.12 11,814.45 
2019 35,016,672 63,309.47 11,592.05 5,174,904 281.4765 
2020 40,502,913 64,512.5 11,903.96 90,578.7 28.93128 
2021 38,272,203 65,738.65 11,907.2 2,727,224 85.35913 
2022 39,064,767 66,988.35 11,971.65 2,344,654 80.86266 
2023 40,220,104 68,262.08 12,053.58 1,603,412 59.19735 
2024 41,834,538 69,560.29 12,144.48 407,213 28.86003 
2025 39,740,167 70,883.45 12,176.15 2,924,002 58.05765 
2026 36,231,481 72,232.06 12,123.2 6,859,329 172.1859 
2027 42,351,577 73,606.61 12,267.86 1,170,141 88.99798 
2028 42,937,717 75,007.59 12,361.27 1,019,218 57.36771 
2029 40,753,817 76,435.53 12,359.13 3,642,688 121.6068 
2030 34,385,659 77,890.94 12,305.39 10,454,811 237.7475 
2031 43,393,787 79,374.36 12,453.17 1,895,088 152.686 
2032 42,508,928 80,886.34 12,582.4 3,232,835 86.47943 
2033 43,116,788 82,427.42 12,652.91 3,082,393 79.32086 
2034 44,713,780 83,998.17 12,733.62 1,947,392 62.26526 
2035 46,585,367 85,599.17 12,798.58 542,416.8 61.28557 
2036 44,148,877 87,231.01 12,839.87 3,450,185 84.29419 
2037 40,034,722 88,894.28 12,804.76 8,040,331 184.0345 
2038 47,027,500 90,589.6 12,964.36 1,528,303 89.37007 
2039 47,975,261 92,317.58 13,026.81 1,066,101 92.19454 
2040 48,116,107 94,078.87 13,099.47 1,415,668 85.1295 
2041 45,450,399 95,874.11 13,128.81 4,576,694 121.709 
2042 50,161,998 97,703.95 13,284.44 365,365.8 32.33329 
2043 49,887,794 99,569.08 13,290.71 1,144,843 92.64186 
2044 49,984,378 101,470.2 13,362.83 1,558,585 87.44587 

Figure 5 displays the results of Scenario 1 including total water demands of the three demand sectors along with the amounts satisfied and unsatisfied in the studied years.
Figure 5

The amount of satisfied and unsatisfied water demands in Scenario 1.

Figure 5

The amount of satisfied and unsatisfied water demands in Scenario 1.

Close modal

Water demand (satisfied/unsatisfied) in Scenario 2

As with Table 3, Table 4 presents the total water requirements of the agricultural, drinking water, and industrial sectors over the studied period for Scenario 2. The table presents the results of Scenario 2 for the satisfied and unsatisfied needs of the three demand sectors for the period 2018–2044.

Table 4

The amount of satisfied and unsatisfied water needs in Scenario 2 (m3)

YearSatisfied
Unsatisfied
AgricultureUrbanIndustryAgricultureUrbanIndustry
2018 37,368,230 62,129.12 11,814.45 
2019 38,917,435 63,309.47 11,760.71 191,512 112.8092 
2020 39,500,037 64,512.5 11,932.89 
2021 39,639,504 65,738.65 11,938.38 255,532.6 54.1748 
2022 40,293,988 66,988.35 12,025.04 27.47368 
2023 40,532,632 68,262.08 12,083.22 164,294.9 29.55804 
2024 41,103,897 69,560.29 12,173.34 
2025 41,207,685 70,883.45 12,175.15 307,250.9 59.05766 
2026 41,088,211 72,232.06 12,186.34 841,874 109.0366 
2027 42,299,164 73,606.61 12,327.25 50,221.37 29.61109 
2028 42,772,880 75,007.59 12,399.46 19.17812 
2029 43,022,980 76,435.53 12,434.05 177,628.1 46.68369 
2030 41,924,809 77,890.94 12,390.43 1,707,806 152.7056 
2031 43,875,847 79,374.36 12,544.87 193,094.1 60.98839 
2032 44,106,754 80,886.34 12,613.14 402,875.8 55.74191 
2033 44,954,726 82,427.42 12,703.46 28.77308 
2034 45,157,214 83,998.17 12,764.57 247,059.6 31.32433 
2035 45,858,316 85,599.17 12,828.5 31.36757 
2036 45,856,380 87,231.01 12,862.78 460,519.9 61.39269 
2037 45,528,597 88,894.28 12,863.07 1,251,472 125.719 
2038 47,134,951 90,589.6 13,022.35 112,918.4 31.38099 
2039 47,695,173 92,317.58 13,086.81 25,175.24 32.19549 
2040 48,197,551 94,078.87 13,161.95 22.64235 
2041 48,353,109 95,874.11 13,200.77 326,417.6 49.74644 
2042 49,166,322 97,703.95 13,316.77 
2043 49,611,118 99,569.08 13,350.85 46,867.68 32.51141 
2044 50,154,565 101,470.2 13,420.12 30.1515 
YearSatisfied
Unsatisfied
AgricultureUrbanIndustryAgricultureUrbanIndustry
2018 37,368,230 62,129.12 11,814.45 
2019 38,917,435 63,309.47 11,760.71 191,512 112.8092 
2020 39,500,037 64,512.5 11,932.89 
2021 39,639,504 65,738.65 11,938.38 255,532.6 54.1748 
2022 40,293,988 66,988.35 12,025.04 27.47368 
2023 40,532,632 68,262.08 12,083.22 164,294.9 29.55804 
2024 41,103,897 69,560.29 12,173.34 
2025 41,207,685 70,883.45 12,175.15 307,250.9 59.05766 
2026 41,088,211 72,232.06 12,186.34 841,874 109.0366 
2027 42,299,164 73,606.61 12,327.25 50,221.37 29.61109 
2028 42,772,880 75,007.59 12,399.46 19.17812 
2029 43,022,980 76,435.53 12,434.05 177,628.1 46.68369 
2030 41,924,809 77,890.94 12,390.43 1,707,806 152.7056 
2031 43,875,847 79,374.36 12,544.87 193,094.1 60.98839 
2032 44,106,754 80,886.34 12,613.14 402,875.8 55.74191 
2033 44,954,726 82,427.42 12,703.46 28.77308 
2034 45,157,214 83,998.17 12,764.57 247,059.6 31.32433 
2035 45,858,316 85,599.17 12,828.5 31.36757 
2036 45,856,380 87,231.01 12,862.78 460,519.9 61.39269 
2037 45,528,597 88,894.28 12,863.07 1,251,472 125.719 
2038 47,134,951 90,589.6 13,022.35 112,918.4 31.38099 
2039 47,695,173 92,317.58 13,086.81 25,175.24 32.19549 
2040 48,197,551 94,078.87 13,161.95 22.64235 
2041 48,353,109 95,874.11 13,200.77 326,417.6 49.74644 
2042 49,166,322 97,703.95 13,316.77 
2043 49,611,118 99,569.08 13,350.85 46,867.68 32.51141 
2044 50,154,565 101,470.2 13,420.12 30.1515 

Figure 6 schematically depicts the total water needs in Scenario 2 along with the satisfied and unsatisfied needs within the study site of Semnan over the studied years.
Figure 6

The amount of satisfied and unsatisfied water needs in Scenario 2.

Figure 6

The amount of satisfied and unsatisfied water needs in Scenario 2.

Close modal

Results of comparing the two scenarios

Based on the results of the two scenarios, the amount of unsatisfied needs decreases in the studied years by modifying the cropping pattern in Scenario 2. Out of the total water need in the study site of Semnan, almost 94.1 percent is satisfied and 5.9 percent is left unsatisfied in Scenario 1 whereas the amount of satisfied need increases to 99.4 percent and the amount of unsatisfied need falls to 0.6 percent in Scenario 2. Therefore, if the cropping pattern is modified at the study site, 5.3 percent of the unsatisfied water need can be met by the internal resources.

Model validation and calibration

In modeling the study site using WEAP, after all data on demand and supply points are inputted into the simulation environment, the simulation should be validated. Since there was no precise and complete information for calibration, the research used the data available for the base year for model calibration. However, the values simulated for the hydrometry station of Jazin (the calculated values) were also compared with real values (registered values), and the error criteria were calculated. For example, RMSE and MAE were estimated at 0.06 and 0.05, respectively. The comparison of the observed values with the real values reveals the desirable capability of the model in simulating water for the study site using WEAP.

The risk assessment for the wastewater (Bayesian network)

Figure 7 displays the qualitative part of the Bayesian network for the two wastewater treatment plants of Semnan and Mehdishahr. The qualitative model of the treatment plant is based on surface aeration in Semnan and active sediment in Mehdishahr. The quantitative phase and the analysis of results gave different results for the two treatment plants due to the differences in the data. As was already mentioned, the GenIE software package was used in this research to analyze the Bayesian network. Figure 7 depicts the cause-and-effect relationships of the primary or parent events, intermediate or generative events, and the final node. The base nodes are causes of the intermediate nodes, so no conditional probability is defined for them. The intermediate nodes have conditional probability, which results from the impact of each previous node. For example, Figure 7 shows that the violation of pH and TSS data reduces soil fertility. So, the probability of soil fertility success or failure realizes if the pH and TSS data are both within the standard ranges or both, or either one violates the standard ranges.
Figure 7

The Bayesian network for the wastewater treatment plants of Semnan and Mehdishahr.

Figure 7

The Bayesian network for the wastewater treatment plants of Semnan and Mehdishahr.

Close modal

The present research determined the probability of a base node's success (S) or failure (F), displayed in green, by calculating the number of violated data to the total data. For example, regarding the pH node connected to soil fertility loss, 70 percent of the data were within the standard range, but 30 percent violated it. These figures were determined by comparing the data with the standard ranges published in Presidential Deputy of Planning and Strategic Monitoring Journal No. 535 (Environmental regulations for the reuse of return water and wastewater) and Journal No. 426 (Guideline to categorize the quality of raw water, wastewater, and return water for industrial and recreational uses). The standard values are given in Table 5.

Table 5

The allowed levels of wastewater consumption in different sectors

ParameterDischarge to surface waterDischarge to absorbent wells (groundwater tables)Agricultural and irrigation usesIndustrial uses (3rd group categorization)
BOD 30 30 100 – 
COD 60 60 200 0–20 
pH 6.5–8.5 5–9 6–8.5 5–10 
DO – – 
NTU (obscurity) 50 – 50 – 
TSS 40 – 100 0–10 
TDS – – – 0–500 
Cl 0.2 – 
EC – – 300 – 
ParameterDischarge to surface waterDischarge to absorbent wells (groundwater tables)Agricultural and irrigation usesIndustrial uses (3rd group categorization)
BOD 30 30 100 – 
COD 60 60 200 0–20 
pH 6.5–8.5 5–9 6–8.5 5–10 
DO – – 
NTU (obscurity) 50 – 50 – 
TSS 40 – 100 0–10 
TDS – – – 0–500 
Cl 0.2 – 
EC – – 300 – 

Results of risk assessment for the wastewater of sewage treatment plants

The conditional tables of the generative nodes were determined by experts’ opinions and the effect of each data on the target node. Finally, after the probabilities of success/failure of each Bayesian node were determined for both treatment plants, the model was analyzed and the risk of the wastewater of the plants was determined for the four sectors of human health, the environment, agriculture, and industry. Figures 8 and 9 display the results of the risk assessment. According to Figure 8, the risk of the Semnan treatment plant is 29 percent for the agricultural sector, 20 percent for the industrial sector, 35 percent for the environmental sector, and 45 percent for the human health sector. The overall risk of the wastewater, which was calculated based on its risk for the four sectors, is 33 percent. Based on Figure 9, the risk of the Mehdishahr treatment plant for the agricultural, industrial, environmental, and human health sectors is 24, 12, 28, and 35 percent, respectively. The overall risk of the Mehdishahr plant's wastewater was estimated at 27 percent. As is evident, the wastewater of both plants can be consumed in the industry more safely. In addition, the wastewater of the Mehdishahr treatment plant is more reliable than that of the Semnan plant.
Figure 8

The results of the risk assessment for the wastewater of the Semnan treatment plant.

Figure 8

The results of the risk assessment for the wastewater of the Semnan treatment plant.

Close modal
Figure 9

The results of the risk assessment for the wastewater of the Mehdishahr treatment plant.

Figure 9

The results of the risk assessment for the wastewater of the Mehdishahr treatment plant.

Close modal

Mehdishahr and Shahmirzad region

Given the existence of three sewage treatment plants in the study site (Shahmirzad, Mehdishahr, and Semnan), it is recommended to use the treated wastewater of the Mehdishahr plant in the industries in the region including Semnan Tile Factory and Pre-fabricated Concrete Manufacturing Factory, which have demanded wastewater purchase according to a report by Semnan province's Water and Sewage Company.

In addition, since Shahmirzad is located in the north of Mehdishahr and has a mountainous climate and its sewage treatment plant with a nominal capacity of 2,400 m3/day, which is at the pilot operation stage, and has the advanced treatment process of extensive aeration active sediment during which N and P are removed, it is recommended to plan for using its wastewater in the Shahmirzad Agro-Industrial Company in order to make savings in the consumption of groundwater resources.

Semnan region

Since the study site has a hot and arid climate and is located in the vicinity of the Kavir, the application of the treated wastewater of Semnan to irrigate urban green spaces can supply the minimum per capita need of urban green spaces. Also, some part of the wastewater can be transferred to the industrial town for the use of water-intensive industries like Zafar Steel Factory, Oghab Bus Manufacturing Factory, and so on, which will result in savings on the abstraction from groundwater tables. As such, the industrial water demand will be partially satisfied by the wastewater, leaving the freshwater wells for the use of regional people considering the drought and water deficit crisis. The next point is camel farming in the Kavir. Presently, these camels may use the wastewater (which is disposed of in the Kavir), putting them at risk of catching parasites and viral infections and transferring them to humans. So, the optimization of wastewater use for the industry will contribute to enhancing the health and food security of the communities.

There are farms around the Semnan Sewage Treatment Plant, which may use its wastewater to irrigate their crops, e.g., wheat and barley, even in untreated form. Since the environmental standard says that the treated wastewater is not allowed to be used in the cultivation of crops, fruit trees, vegetables, and any items that are directly related to people's food baskets, it is recommended to conduct soil tests and determine the trees that can be cultivated in the region to use their wood in furniture production and can contribute to improving regional ecosystems and alleviating climate change.

Risk and reliability of the wastewater (Semnan and Mehdishahr)

This research presented two models for assessing the risk and reliability of the wastewater produced by sewage treatment plants. The two treatment plants of Semnan and Mehdishahr were selected as the case study. The input data for the models were derived from the opinions of treatment plant experts and specialists and the field data of the treatment plants. After the two Bayesian network models were analyzed, the results of the risk assessment gave 27% for the Mehdishahr plant and 33% for the Semnan plant, showing the better performance of the latter treatment plant. It was also found that the output wastewater of both plants could be used in the industrial sector more safely with less risk. The models provided to the treatment plant managers and users can help them determine solutions for increasing the reliability of wastewater use by determining the risk of their use.

The scenario of irrigation

The comparison of the scenario of irrigation efficiency enhancement by applying modern irrigation systems and/or changing the cropping pattern versus the current cropping pattern and irrigation method shows that when the study site of Semnan is faced with rainfall shortage and drought, the latter scenario can be a turning point to prevent groundwater over-abstraction. However, as was mentioned, the study site had an arid climate and the application of polluted water by the agricultural sector and, especially, the industrial sector can partially meet the regional water demand from groundwater and surface water resources.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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500
.
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