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- Al-Isawi, Rawaa H K, et al.
(författare)
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Assessment of diesel-contaminated domestic wastewater treated by constructed wetlands for irrigation of chillies grown in a greenhouse
- 2016
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Ingår i: Environmental Science and Pollution Research. - : Springer Science and Business Media LLC. - 0944-1344 .- 1614-7499. ; 23:24, s. 25003-25023
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Tidskriftsartikel (refereegranskat)abstract
- In order to avoid environmental pollution and eliminate the need for using fertiliser, this study assessed for the first time the optimum performance of mature (in operation since 2011) vertical flow constructed wetlands in treating domestic wastewater (with and without hydrocarbon) and the subsequent recycling of the outflow to irrigate chillies (De Cayenne; Capsicum annuum (Linnaeus) Longum Group ‘De Cayenne’) grown in a greenhouse. Various variables were investigated to assess the treatment performance. Concerning chilli fruit numbers, findings showed that the highest fruit yields for all wetland filters were associated with those that received inflow wastewater with a high loading rate, reflecting the high nutrient availability in treated wastewater, which is of obvious importance for yield production. Findings also indicated that wetlands without hydrocarbon, small aggregate size, low contact time and low inflow loading rate provided high marketable yields (expressed in economic return). In comparison, chillies irrigated by filters with hydrocarbon contamination, small aggregate size, high contact time and high loading rate also resulted in high marketable yields of chillies, which pointed out the role of high contact time and high inflow load for better diesel degradation rates.
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| 2. |
- Al-Isawi, Rawaa H.K., et al.
(författare)
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Prediction of Indoor Environmental Quality Using a Regression Model for Educational Buildings in Hot Arid Climate : A Case Study in the Al-Najaf Technical Institute – Iraq
- 2022
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Ingår i: Environmental Research, Engineering and Management. - : Kaunas University of Technology (KTU). - 1392-1649 .- 2029-2139. ; 78:1, s. 117-132
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Tidskriftsartikel (refereegranskat)abstract
- In hot climates, achieving a good indoor environmental quality (IEQ) in existing buildings is important especially with climate change challenges as future heat waves will increase in frequency, duration, and intensity. In educational buildings, there is much more focus on the IEQ parameters and the interactions among them that need to be in line with the continuously changing learning environment. This study assesses the IEQ parameters (represented by noise, temperature and humidity) at three selected campus areas (lecture rooms of an administrative department building (LR), main hall of a management department building (MH) and a central library building (CL)) at the Al-Najaf Technical Institute (NTI), Al-Najaf City, Iraq, for the period from May to December 2019. A statistical analysis using a multi-linear regression model was performed to determine the relationship between the selected IEQ parameters and explain the noise level behavior as a function of the temperature and relative humidity. The research indicated that the noise levels and temperature values exceeded the maximum standard limits in all buildings reflecting the displeasing sound and heating quality within the studied areas, while the readings for relative humidity within each building environment complied with standards. Moreover, for both LR and MH buildings (R2 ≥ 0.8, significance F ≤ 0.01), the noise values were satisfactorily modeled by temperature and relative humidity highlighting the interactions between temperature, humidity and noise under consistent conditions. However, the results for the CL building (R2 = 0.6, significance F = 0.1) showed no relationship between the IEQ parameters, highlighting the fact that this building is exposed to unsteady conditions (an irregular number of people using this building during the daytime) resulting in a high variation of data measurements. The current results demonstrate that detailed modeling can be helpful to predict IEQ parameters depending on other known parameters in buildings. The results of the predictive model aligned with the directly measured data. Therefore, its performance is equally effective, but with a significant reduction in cost and time consumed.
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