Examining Relationships Between Socioeconomic Factors and Landscape Metrics in the Southern Basin of the Caspian Sea

Socioeconomic forces are not only among the main drivers of landscape dynamics; they are also influenced by landscape patterns. Landscape structure and functions are closely related to natural and social factors. The objective of this study was to investigate the relationships among some human-related factors and landscape ecological metrics as landscape pattern indicators and to identify suitable metrics for modeling these relationships. To this goal, landscape ecological metrics were calculated for each of the 32 counties of Mazandaran and Guilan provinces located in the southern basin of the Caspian Sea using land use/cover maps in class level. Stream network metrics were calculated using a digital elevation model, road density metrics were calculated using map of main roads separately, and significant metrics were selected according to results of correlation tests and factor analysis. The correlations between these metrics and socioeconomic factors were tested, and their relationships were modeled with multiple linear regressions. Significant relationships were found among socioeconomic factors and landscape ecological metrics, and land use/cover data are applicable for modeling socioeconomic factors, especially demographic and employment structure factors. Among the landscape metrics applied in this study, road density, mean patch size, mean nearest neighbor distance, and percentage of a land use/cover class in landscape were important metrics for predicting socioeconomic factors. Our findings indicated that road density metric and percentages of urban class are useful for predicting urban socioeconomic factors and percentage of agriculture and forest classes in the landscape are suitable metrics for predicting rural socioeconomic factors.     

Spatiotemporal analysis of COVID-19, air pollution,climate, and meteorological conditions in a metropolitan region of Iran

The COVID-19 pandemic has a close relationship with local environmental conditions. This study explores the effects of climate characteristics and air pollution on COVID-19 in Isfahan province, Iran. A number of COVID-19 positive cases, main air pollutants, air quality index (AQI), and climatic variables were received from March 1, 2020, to January 19, 2021. Moreover, CO, NO₂, and O₃ tropospheric levels were collected using Sentinel-5P satellite data. The spatial distribution of variables was estimated by the ordinary Kriging and inverse weighted distance (IDW) models. A generalized linear model (GLM) was used to analyze the relationship between environmental variables and COVID-19. The seasonal trend of nitrogen dioxide (NO₂), wind speed, solar energy, and rainfall like COVID-19 was upward in spring and summer. The high and low temperatures increased from April to August. All variables had a spatial autocorrelation and clustered pattern except AQI. Furthermore, COVID-19 showed a significant association with month, climate, solar energy, and NO₂. Suitable policy implications are recommended to be performed for improving people’s healthcare and control of the COVID-19 pandemic. This study could survey the local spread of COVID-19, with consideration of the effect of environmental variables, and provides helpful information to health ministry decisions for mitigating harmful effects of environmental change. By means of the proposed approach, probably the COVID-19 spread can be recognized by knowing the regional climate in major cities. The present study also finds that COVID-19 may have an effect on climatic condition and air pollutants.

     

From disaster to development: a systematic review of community‐driven humanitarian logistics

A plethora of untapped resources exist within disaster‐affected communities that can be used to address relief and development concerns. A systematic review of the literature relating to community participation in humanitarian logistics activities revealed that communities are able to form ad hoc networks that have the ability to meet a wide range of disaster management needs. These structures, characterised as Collaborative Aid Networks (CANs), have demonstrated efficient logistical capabilities exclusive of humanitarian organisations. This study proposes that CANs, as a result of their unique characteristics, present alternatives to established humanitarian approaches to logistics, while also mitigating the challenges commonly faced by traditional humanitarian organisations. Furthermore, CANs offer a more holistic, long‐term approach to disaster management, owing to their impact on development through their involvement in humanitarian logistics. This research provides the foundation for further theoretical analysis of effective and efficient disaster management, and details opportunities for policy and practice.     

Comparing different environmental variables in predictive models of bird distribution

The response variable (often the presence of a species) in predictive habitat models relies on a set of environmental predictors. Among all known environmental predictors, vegetation has the most effect on species abundance and on their habitat preferences, due to the wide range of necessary resources that it provides for the survival of bird species. However, other predictors, in turn, affect bird distribution, and some-times they play a more important role in habitat selection, depending on the natural history and ecological needs of the bird species. In this regard, different analyses have been conducted to predict the distribution, and define habitat suitability (such as discriminant function analysis, General Linear Models, and ANOVA). In this study, all three analytical designs were used to investigate the relationship of seven bird species to the major environmental gradients in the study area, to find out the significance of each of these factors on habitat selection. GIS has been used to prepare spatial distributional data, and to overlay and calculate different aspects of the environmental factors. The results suggest that potential individual habitat patches play a small role compared to the landscape (entire corresponding habitat patches), when considering vegetation. The influence of built-up areas is significant for all the species, and the proximity to the sea shore is very significant for at least one of the species, however, it is not neutral for all other species.     

Fuzzy AHP approach for prioritizing electronic waste management options: a case study of Tehran,Iran

Environmental Science and Pollution Research - Electronic waste (E-waste) can be considered as challenging solid waste streams especially in some developing countries, including Iran. Several...     

Modeling Water Quantity and Sulfate Concentrations in the Devils Lake Watershed Using Coupled SWAT and CE‐QUAL‐W2

Devils Lake is an endorheic lake in the Red River of the North basin in northeastern North Dakota. During the last two decades, the lake water level has risen by nearly 10 m, causing floods that have cost more than 1 billion USD in mitigation measures. Another increase of approximately 1.5 m in the lake water level would cause spillage into the Sheyenne River. To alleviate this potentially catastrophic spillage, two artificial outlets were constructed. However, the artificial drainage of water into the Sheyenne River raises water quality concerns because the Devils Lake water contains significantly higher concentrations of dissolved solids, particularly sulfate. In this study, the Soil and Water Assessment Tool (SWAT) was coupled with the CE‐QUAL‐W2 model to simulate both water balance and sulfate concentrations in the lake. The SWAT model performed well in simulating daily flow in tributaries with E_NS > 0.5 and |PBIAS| < 25%, and reproduced the lake water level with a root mean square error of 0.35 m for the study period from 1995 to 2014. The water temperature and sulfate concentrations simulated by CE‐QUAL‐W2 for the lake are in general agreement with the field observations. The model results show that the operation of the two outlets since August 2005 has lowered the lake level by 0.70 m. Furthermore, the models show pumping water from the two outlets raises sulfate concentrations in the Sheyenne River from ~100 to >500 mg/L. Editor's note : This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.     

Heterotrophic Biological Denitrification Using Microbial Cellulose as Carbon Source

The objective of this study was to investigate the feasibility of using a microbial biopolymer produced by Acetobacter xylinum as a carbon source for heterotrophic biological denitrification. The denitrification rate, COD availability and nitrite concentration were response parameters. Under the experimental conditions, a denitrification rate of about 0.74 kg NO₃ ⁻N/m³d at 6 h retention time was achieved with microbial cellulose (MC). The reactor effluent contained significantly COD concentrations (20–86 mg/L) so it was not carbon limited, and was receiving enough carbon to facilitate the denitrification process. The maximum nitrite concentration in the effluent was found to be 0.4 mg/L. However, decreasing the retention time to 3 h significantly reduced the efficiency. It can be concluded that the MC is a suitable carbon source for nitrate removal in a heterotrophic biological denitrification process.