Application of exploratory and Spatial Data Analysis (SDA), singularity matrix analysis, and fractal models to delineate background of potentially toxic elements: A case study of Ahvaz, SW Iran

Contamination, ecological-health risks, and sources of potentially toxic elements in road-dust sediments and soils of the largest urban riverfront scenic park in China

Identifying the contamination and sources of potentially toxic elements (PTEs) in road-dust sediment (RDS) and the surrounding greenspace soil of urban environments and understanding their ecological-health risks are important for pollution management and public health. The contamination characteristics, ecological and probabilistic health risks, and source apportionment of eight PTEs (Cd, Pb, Cr, Cu, Ni, As, Zn, and Hg) in the Yellow River Custom Tourist Line of Lanzhou, which is the largest open urban riverfront scenic park in China, were investigated. The results showed that all the RDS PTE mean concentrations exceeded their soil background values, whereas for the surrounding greenspace soils, the concentrations of the PTEs, except for Cr and Ni, were also higher than their local background levels. Moreover, the RDS-soil system was mainly contaminated by Cd, Zn, Pb, Cu, and Hg to varying degrees, and the integrated ecological risks of PTEs in the RDS and soil were high and considerable at most sites, respectively. The probabilistic health risk assessment results demonstrated that the non-carcinogenic hazard risk for humans was negligible, but the total carcinogenic risks should be considered. Source apportionment using a positive matrix factorization model combined with multivariate statistical analyses revealed that Cr, Ni, and As in the RDS-soil system were from natural and industrial sources, Cd, Pb, Zn, Cu came from vehicle emissions and pesticide and fertilizer applications, and Hg was from natural and industrial sources and utilization of pesticides with fertilizers. This work provides scientific evidence for urban planning and human health protection in urban environments.

A neighborhood-level analysis of association between social vulnerability and COVID-19 in ahvaz, Iran

Social vulnerability and society's resilience are two concepts frequently used to examine the capacity of social systems to prepare, absorb, and adapt to environmental hazards and shocks. With the emergence of the COVID-19 pandemic, the role of social vulnerability in dealing with risks has gained renewed attention. Assessing social vulnerability can help managers and planners prioritize budgets, develop prevention programs, and enhance risk preparedness. This study aimed to determine the association between social vulnerability and COVID-19 in the neighborhoods of Ahvaz, Iran. To assess the social vulnerability of Ahvaz neighborhoods, decision-making techniques (best-worst method and weighted aggregated sum product assessment method) and geographic information systems were applied. Moreover, to investigate the relationship between social vulnerability and COVID-19 cases, the Pearson correlation test was used. The results showed that the '20-meteri shahrdari' neighborhood has the highest level of social vulnerability, and the lowest level of social vulnerability among the neighborhoods of Ahvaz belongs to the neighborhood of 'Shahrak Naft'. There is a low inverse association between the integrated index of social vulnerability and the incidence of COVID-19 per 1000 people in Ahvaz. By revealing the most important details at the neighborhood level and levels of vulnerability, the results can inform effective planning actions at the neighborhood level.

A Computational Intelligence Approach to Predict Energy Demand Using Random Forest in a Cloudera Cluster

Society’s energy consumption has shot up in recent years, making the prediction of its demand a current challenge to ensure an efficient and responsible use. Artificial intelligence techniques have proven to be potential tools in handling tedious tasks and making sense of large-scale data to make better business decisions in different areas of knowledge. In this article, the use of random forests algorithms in a Big Data environment is proposed for household energy demand forecasting. The predictions are based on the use of information from different sources, confirming a fundamental role of socioeconomic data in consumer’s behaviours. On the other hand, the use of Big Data architectures is proposed to perform horizontal and vertical scaling of the solution to be used in real environments. Finally, a tool for high-resolution predictions with great efficiency is introduced, which enables energy management in a very accurate way.

Spatial and temporal analysis of urban heat island using Landsat satellite images

The average land surface temperature (LST) of Earth has increased since the late nineteenth century due to the warming of the Earth's atmosphere. Increased surface temperatures, especially in cities, are a significant environmental problem that intensifies urban heat islands (UHIs). In this study, land surface temperature, urban thermal field variance index (UTFVI), and UHI index were mapped using Landsat 4, 5, 7, and 8 satellite images to identify the distribution and determine the intensities of the UHI. Maps of land use at multi-year intervals between 1995 and 2016 were created using the support vector machine (SVM) method. These were used to compare LST variations to land-use changes and to determine the linkages between the two. The results showed that the highest recorded temperatures in Ahvaz, the capital of Khozestan Province, Iran, occurred in areas of bare land (42.93°C) and residential development (40.06°C) in 2017. Land use classification showed that the highest classification accuracy (in 2016) was 93%. The most varying extents of land use in Ahvaz were bare lands, residential lands, and green spaces. Green spaces in the study area in 1995 and 2016 covered 14% and 7% of the area, respectively, which showed a 50% reduction in green space over 21 years. A composite map of UTFVI and UHI showed that the locations classified as very hot had the worst UTFVI. The results of this study of Ahvaz, Iran's heat islands, can inform and guide urban planners in locational matters and in efforts to mitigate and adapt changing land uses in order to limit the intensification of the UHI.

Assessment of the potential ecological and human health risks of heavy metals in Ahvaz oil field, Iran

The potential hazard to human health from exposure to heavy metals in surface soil was assessed using 66 soil samples collected from Ahvaz oil field. To this end, the contents of heavy metals were measured by the inductively coupled plasma spectroscopy (ICP-OES). Mean levels of As, Cd, Co, Cr, Cu, Ni, Pb, V, and Zn were 5.9, 0.4, 7.1, 36.5, 41.2, 39.8, 67.4, 31.5, and 77.6 mg/kg, respectively. Contents of all studied heavy metals, with the exception of Co, Cr, and V, were several times higher than that of baselines. Correlation coefficients and principal component analysis (PCA) identified two main groups as sources of heavy metals in the surface soil of Ahvaz oil-field. Metals such as Co, Cr, and V were observed to originate from natural sources and As, Cd, Cu, Ni, Pb, and Zn originated from anthropogenic sources such as petroleum leakage and the pollution caused by drilling mud from oil wells. Pb and Zn were of significantly high EF mean enrichment value, and Co, Cu, Cd, and As had high enrichment in surface soil. Pb, Cr, V, Zn, Co, Cu, Ni, and As had a low potential ecological risk (PER) whereas Cd had a moderate PER. The risk of carcinogenic and non-carcinogenic diseases was detected to be higher in children than in adults. The carcinogenic risk (Cr) calculation was more than 1 × 10^-6 for children and adults. Additionally, the CR of Cr for both children and adults indicated risk under control conditions.