A GIS-Based Spatiotemporal Modelling of Urban Traffic Accidents in Tabriz City during the COVID-19 Pandemic

A spatiotemporal analysis of personal casualty accidents in China's electric power industry

The electric power industry in China has experienced significant growth in recent years. Despite efforts to improve safety management in the industry, accidents still occur frequently. This study aimed to analyze the personal casualty accidents in the electric power industry from 2012 to 2021. Specific methods used include descriptive analysis, principal component analysis, and Theil index model. The results indicated that fall, electric shock, and collapse were the primary types of accidents, accounting for 59.65 % of all accidents. Accidents were higher in April and August, but lower in February. While the accident rate was relatively low on Mondays, the fatality rate was higher on Mondays, Thursdays, and Fridays. Taking into account accidents, workload, and labor, we found that Ningxia, Hainan, and Guangxi exhibited subpar levels of safety management within the electric power industry. The overall difference in the number of deaths in 31 provinces was significant in 2012 and 2016. It was significantly reduced in 2021. In terms of the proportion of intraregional and interregional differences, there were significant differences in the number of accidents and fatalities between provinces in the Central China and North China regions. This study provides valuable insights for enterprises to formulate accident prevention strategies and for the government to develop relevant policies.

An optimized development of urban air quality monitoring network design based on particulate matters

The design of an air quality monitoring network (AQMN) is the mandatory step to manage air pollution in megacities. Several studies are being done on the location selection of AQMNs based on topography, meteorology, and pollution density. Still, the critical research gap that needs to be addressed is the role of pollutants' importance and prioritization in AQMN. This study aims to utilize the sphere of influence (SOI) method to design an AQMN in a megacity based on particulate matter (PM) as the most serious urban pollutant. Model evaluation was done by employing annual emission inventory data of PM in Tabriz, an industrial and crowded megacity with high exposure to salt particulates, considering 3549 square blocks with a size of 500 m * 500 m. Then, the SOI methodology utilizing the utility function (UF) approach is applied using MATLAB software calculations to determine optimal air quality monitoring network configurations. A range of numbers of utility functions was yielded for every spot on the map. It resulted in grid city maps with final spots for PM10, PM2.5, and intersecting spots. As a result, ten sites are selected as the best possible locations for the AQMN of a 2 million population city. These results could play a precise and significant role in urban air quality decision-making and management.

Impacts of the Urmia Lake Drought on Soil Salinity and Degradation Risk: An Integrated Geoinformatics Analysis and Monitoring Approach

Recent improvements in earth observation technologies and Geographical Information System (GIS) based spatial analysis methods require us to examine the efficiency of the different data-driven methods and decision rules for soil salinity monitoring and degradation mapping. The main objective of this study was to analyze the environmental impacts of the Lake Urmia drought on soil salinity and degradation risk in the plains surrounding the hyper-saline lake. We monitored the impacts of the lake drought on soil salinity by applying spatiotemporal indices to time-series satellite images (1990–2020) in Google Earth Engine environment. We also computed the soil salinity ratio to validate the results and determine the most efficient soil salinity monitoring techniques. We then mapped the soil degradation risk based on GIS spatial decision-making methods. Our results indicated that the Urmia Lake drought is leading to the formation of extensive salt lands, which impact the fertility of the farmlands. The land affected by soil salinity has increased from 2.86% in 1990 to 16.68% in 2020. The combined spectral response index, with a performance of 0.95, was the most efficient image processing method to assess soil salinity. The soil degradation risk map showed that 38.45% of the study area has a high or very high risk of degradation, which is a significant threat to food production. This study presents an integrated geoinformation approach for time-series soil salinity monitoring and degradation risk mapping that supports future studies by comparing the efficiency of different methods as state of the art. From a practical perspective, the results also provide key information for decision-makers, authorities, and local stakeholders in their efforts to mitigate the environmental impacts of lake drought and sustain the food production to sustain the 7.3 million residents.