Investigation of SARS-CoV-2 RNA contamination in water supply resources of Tabriz metropolitan during a peak of COVID-19 pandemic.

It is crucial to have access to clean water resources during the COVID-19 pandemic for hygiene, since virus infection through wastewater leaks in metropolitan areas can be a threat. Accurate monitoring of urban water resources during the pandemic seems to be the only way to confirm safe and infected resources. Here, in this study, the amount of Severe Acute Respiratory Syndrome Coronavirus 2's Ribonucleic Acid (SARS-CoV-2 RNA) in the Tabriz urban water network located in the northwest of Iran was investigated by an extensive sampling of the city's water sources at a severe peak of the COVID-19 pandemic. The sampling process comprised a range of water sources, including wells, qanats, water treatment facilities, dams, and reservoirs. For each sample, a combination of polyethylene glycol (PEG) and sodium chloride (NaCl) was used for concentration and a laboratory RNA-based method was conducted for quantification. Before applying the extraction and quantification procedure to real samples, the proposed concentration method was verified with synthetic serum samples for the first time. After the concentration, RNA extraction was done by the BehPrep extraction column method, and Reverse Transcription Polymerase Chain Reaction (RT-PCR) detection of the virus was done by Covitech COVID-19 RT-PCR kit. In none of the water supply resources, SARS-COV-2 RNA has been detected except in a sample grabbed from a well adjacent to an urban wastewater discharge point downstream. The results of molecular analysis for the positive sample showed that the CT value and concentration of the virus genome were equal to 32.57 and 5720 copies/L, respectively. Quantitative analysis of real samples shows that the city's water network was safe at the time of the study. However, given that the positive sample was exposed to wastewater leakage, periodic sampling from wells and qanats is suggested during the pandemic until it can be proven that the leakage to these water sources is impossible.

Linking plant and soil indices for water stress management in black gram.

Measurement of plant and soil indices as well as their combinations are generally used for irrigation scheduling and water stress management of crops and horticulture. Rapid and accurate determination of irrigation time is one of the most important issues of sustainable water management in order to prevent plant water stress. The objectives of this study are to develop baselines and provide irrigation scheduling relationships during different stages of black gram growth, determine the critical limits of plant and soil indices, and also determine the relationships between plant physiology and soil indices. This study was conducted in a randomized complete block design at the four irrigation levels 50 (I1), 75 (I2), 100 (I3 or non-stress treatment) and 125 (I4) percent of crop's water requirement with three replications in Urmia region in Iran in order to irrigation scheduling of black gram using indices such as canopy temperature (Tc), crop water stress index (CWSI), relative water content (RWC), leaf water potential (LWP), soil water (SW) and penetration resistance (Q) of soil under one-row drip irrigation. The plant irrigation scheduling was performed by using the experimental crop water stress index (CWSI) method. The upper and lower baseline equations as well as CWSI were calculated for the three treatments of I1, I2 and I3 during the plant growth period. Using the extracted baselines, the mean CWSI values for the three treatments of I1, I2 and I3 were calculated to be 0.37, 0.23 and 0.15, respectively, during the growth season. Finally, using CWSI, the necessary equations were provided to determine the irrigation schedule for the four growing stages of black gram, i.e. floral induction-flowering, pod formation, seed and pod filling and physiological maturity, as (Tc - Ta)c = 1.9498 - 0.1579(AVPD), (Tc - Ta)c = 4.4395 - 0.1585(AVPD), (Tc - Ta)c = 2.4676 - 0.0578(AVPD) and (Tc - Ta)c = 5.7532 - 0.1462(AVPD), respectively. In this study, soil and crop indices, which were measured simultaneously at maximum stress time, were used as a complementary index to remove CWSI constraints. It should be noted that in Urmia, the critical difference between the canopy temperature and air temperature (Tc - Ta), soil penetration resistance (Q), soil water (SW) and relative water content (RWC) for the whole growth period of black gram were - 0.036 °C, 10.43 MPa and 0.14 cm3 cm-3 and 0.76, respectively. Ideal point error (IPE) was also used to estimate RWC, (Tc - Ta) and LWP as well as to select the best regression model. According to the results, black gram would reduce its RWC less through reducing its transpiration and water management. Therefore, it can be used as a low-water-consuming crop. Furthermore, in light of available facilities, the farmer can use the regression equations between the obtained soil and plant indices and the critical boundaries for the irrigation scheduling of the field.

Generalized gene expression programming models for estimating reference evapotranspiration through cross-station assessment and exogenous data supply.

Adopting methodologies utilizing exogenous data from ancillary stations for determining crop water requirement is a suitable approach to exempt local shortcomings due to the lack of meteorological data/stations. Meanwhile, soft computing techniques might be suitable tools to be used with such data management scenarios. The present paper aimed at evaluating the generalizability of the gene expression programming (GEP) technique for estimating reference evapotranspiration (ET0) through cross-station assessment and exogenous data supply, using data from Turkey and Iran. The GEP-based models were established and learnt using data from 10 stations in Turkey, and then the developed models were tested (validated) in 18 stations of Iran with considerable latitude differences. Different time periods (beginning and the end of time series) were selected for the training and testing stations so that there was no overlap among the dates of the events in both the groups. A comparison was also performed between the GEP models and the corresponding commonly used empirical equations. The obtained results revealed that the generalized GEP models presented promising outcomes in simulating daily ET0 values when they were trained and tested in quite distant stations with different chronological periods of the applied parameters. The performance accuracy of the empirical equations calibrated using exogenous data was reduced in comparison with their original (non-calibrated) versions. Further, although the generalization ability of the GEP models was reduced when the climatic context of the training-testing stations was different, the overall performance accuracy of those models was higher than those of the commonly used classic empirical equations.

Estimation of evaporation from saline water.

Evaporation, as the main source of water loss from closed lakes, makes a significant contribution to the water balance equation of the lake and can lead to changes in the chemical composition thereof. The objective of the study was to develop an equation for estimation of evaporation from the water surface with different depths and concentrations. To that end, 48 barrels were used to model evaporation at 6 different depths and 8 different concentrations of salinity. The experiments have been conducted in the same meteorological condition for all the barrels near the Urmia Lake. Data were collected in March 1, 2019, to Aug 31, 2019. Different equations fitted to data for each concentrations of salinity separately with different depths, and the equations with the least errors were selected. A model was then developed for the estimation of evaporation, considering the effect of salinity and depth, and the results were compared with daily measurements. The results were evaluated using the root mean square error (RMSE), correlation coefficient (CC), and Nash-Sutcliffe efficiency coefficient (NS). The results indicated that evaporation (Horizontal row) from water surface with high concentrations of salinity to low concentrations of salinity in different depths had an incremental trend. However, it can be seen in the vertical row that evaporation increased from low depth to high depth, and then decreased at a certain depth (120 cm) while the maximum evaporation rate belonged to 90-cm barrels for each concentration of salinity (in the vertical and horizontal row). At the end, the comparison of evaporation computed from the model and measured data showed that the model estimated evaporation at different depths and concentrations of salinity satisfactorily.