Modeling land use/cover change based on LCM model for a semi-arid area in the Latian Dam Watershed (Iran).

The monitoring and modeling of changes, based on a time-series LULC approach, is fundamental for planning and managing regional environments. The current study analyzed the LULC changes as well as estimated future scenarios for 2027 and 2037. To achieve accuracy in predicting LULC changes, the Land Change Modeler (LCM) was used for the Latian Dam Watershed, which is located approximately in the northeast of Tehran. The LULC time-series technique was specified utilizing four atmospherically endorsed surface reflectance Landsat images for the years t1 (1987), t2 (1998), t3 (2007), and t4 (2017) to authenticate the LULC predictions, so to obtain estimates for t5 (2027) and t6 (2037). The LULC classes identified in the watershed were water bodies, build-up areas, vegetated areas, and bare lands. The dynamic modeling of the LULC was based on a multi-layer perceptron (MLP), the neural network in LCM, which presented good results with an average accuracy rate equivalent to 84.89 percent. The results of the LULC change analysis showed an increase in the build-up area and a decrease in bare lands and vegetated areas within the duration of the study period. The results of this research could help in the formulation of public policies designed to conserve environmental resources in the Latian Dam Watershed and, consequently, minimize the risks of the fragmentation of orchards and vegetated areas. Also, careful regional planning ensuring the preservation of natural landscapes and open spaces is critical to creating a resilient regional environment and sustainable development.

An evaluation of the marine environmental resilience to the north of Qeshm Island.

There is always an adamant need to comprehend and draw the complex challenges of sustainability in order to help organize studies, due to the increasing human-related pressures on coastal zones. Hence, by formulating such a comprehensive framework, it could be possible to anticipate changes and support managerial decisions, as well as the degree of resilience of the region's environment. One of the approaches utilized in littoral or coastal zones is the conceptual framework of drivers, pressure, status, impact, and responses (DPSIR)..Qeshm Island, the largest island in the Persian Gulf, is accounted for being the most vital and strategic areas of the mentioned region. In recent decades, Qeshm has become one of the major cultural, natural, geological, and tourism hubs of the country due to its unique regional characteristics, along with its biodiversity and environmental sensitivity. Thereby, in the present research, a combined approach shall be followed to explore the resilience of the marine environment on the northern coast of Qeshm Island by taking advantage of the socioeconomic criterion. In this respect, the conceptual framework of the DPSIR model is utilized in combination with the structural equation model (SEM-PLS) (or partial least squares), which is one of the nonexperimental techniques, to quantify the results in the best manner possible. On the basis of the fuzzy cognitive map (FCM), the regional economic index bearing the weights of 0.62, 0.62, and 0.5, along with an institutional-managerial and biological index, respectively, denotes a two-way positive correlation, whereas this factor has a two-way, but adverse correlation, relationship with a weight of 0.65 in terms of the sociocultural index. Similarly, there is also a one-way and negative relationship, as to the economic index, with a weight of 0.69 which is in relevance with the physio-chemical index.

Optimization of the electro-photocatalytic process for the removal of formaldehyde from water using the Taguchi model.

The most significant environmental issue in many nations across the world is industrial wastewater contamination with formaldehyde (a priority pollutant). Any natural water that has had industrial effluent discharged into with formaldehyde concentrations between 100 and 1000 mg/l is deemed toxic to humans. This is an applied analytical research project aimed at examining formaldehyde removal from urban drinking water using a batch electro-photocatalytic (EPC) reactor that uses ultraviolet-A (UV-A) lamp dynode and immobilized ZnO NPs on a zinc sheet-copper electrode. pH, formaldehyde content, lamp intensity, radiation duration, lamp-electrode distance, ZnO NP stacking, and current density are the factors under investigation. They were found to be in the ranges 3-11, 110-330 mg/l, 480-720 mW/cm2, 8-32 min, 1.5 cm, 1-3, and 4-12 mA/cm2, respectively. The findings demonstrate the relationship between UV-A lamp intensity, radiation duration, and current density with the elimination of formaldehyde. The experimental data better fit a first-order reaction (R2 = 0.9982). The most optimal conditions elimination (0 mg/l) of formaldehyde are achieved at pH = 11, radiation period = 8 min, two layers of ZnO NPs, and current density = 8 mA/cm2 by the Taguchi model. The results show that increasing pH, radiation period, lamp intensity, and current density all increase removal efficiency. The results show that EPC is a practical and efficient method for treating formaldehyde-contaminated drinking water at high concentrations.

Factors affecting the COVID-19 risk in the US counties: an innovative approach by combining unsupervised and supervised learning.

The COVID-19 disease spreads swiftly, and nearly three months after the first positive case was confirmed in China, Coronavirus started to spread all over the United States. Some states and counties reported high number of positive cases and deaths, while some reported lower COVID-19 related cases and death. In this paper, the factors that could affect the risk of COVID-19 infection and death were analyzed in county level. An innovative method by using K-means clustering and several classification models is utilized to determine the most critical factors. Results showed that longitudinal coordinate and population density, latitudinal coordinate, percentage of non-white people, percentage of uninsured people, percent of people below poverty, percentage of Elderly people, number of ICU beds per 10,000 people, percentage of smokers were the most significant attributes.