Sensor networks using the Internet of Things (IoT) are gaining momentum for real-time monitoring of the environment. Increased use of natural resources due to a rise in agriculture production, manufacturing, and civil infrastructure poses a challenge to sustainable growth and development of the global economy. For sustainable use of natural resources (including air, soil, and water), data-driven modeling is needed to understand and simulate contaminant transport and proliferation. Different logging devices are specifically designed to integrate with environmental sensors that send real-time data to the cloud using IoT systems for monitoring. The IoT systems use an LTE network or Wi-Fi to transmit air, water, and soil quality data to the cloud networks. This seamless integration between the logging devices and IoT sensors creates an autonomous monitoring system that can observe environmental parameters in real-time. Various federal organizations and industries have implemented the IoT-based sensor network to monitor real-time air quality parameters (particulate matter, gaseous pollutants), water quality parameters (turbidity, pH, temperature, and specific conductance), and soil parameters (moisture content, soil nutrients). Although several organizations have used IoT systems to monitor environmental parameters, a proper framework to make the monitoring systems reliable and cost-efficient was not explored. The main objective of this study is to present a framework that combines a sensing layer, a network layer, and a visualization layer, allowing modelers and other stakeholders to observe a progressive trend in environmental data while being cost-efficient. This efficient real-time monitoring framework with IoT systems helps in developing robust statistical and mathematical models. The sustainable development of smart cities while maintaining public health requires reliable environmental monitoring data that can be possible by the proposed IoT framework.

This study aimed to conduct a drought trend analysis using the standardized precipitation evapotranspiration index (SPEI) in two mountainous and cold-climate regions in Iran and the United States (US). The Mann–Kendall test was employed to assess the trend in the Upper Colorado River Basin (UCRB) in the US and Lorestan province. The results reveal a predominantly decreasing trend in drought occurrences across Lorestan, especially in southern and southwestern areas with lower elevations. In contrast, the UCRB showed a positive trend, indicating a wet period. The western parts of the UCRB were predominantly affected by droughts. Among the stations, the Khorram Abad station exhibited the most statistically significant trend at the 99% confidence level (Z > 2.57). A temporal trend analysis of droughts revealed more positive and negative abrupt changes in the UCRB than in Lorestan. This indicates a higher degree of small-scale variability in the UCRB compared to Lorestan. This study indicates that factors such as elevation, land use changes, and proximity to water sources may contribute to the observed variations in drought trends. Additionally, the findings highlight that rising temperatures have a significantly greater impact on drought severity than reductions in precipitation. This study provides a temperature-responsive method for drought assessments, supporting the development of adaptive strategies that address snowmelt variability, seasonal water availability, and shifting drought patterns in cold regions.

Removal of synthetic dyes from wastewater is essential both from the environmental and human health point of view. A small concentration of synthetic dyes can reduce water transparency and consequently influence photosynthesis and alter aquatic ecosystems. Acid black 48 is an Azo dye that falls under the category of synthetic dyes used in the textile industry. With dyes, coffee wastewater has high chemical oxygen demand (COD) that can affect dissolved oxygen (DO) in surface waters. A mixture of wastes in surface waters creates a need to investigate the efficiency of existing treatment methods and optimize them. Adsorption using activated carbon is a conventional method used to remove dyes and heavy metals from wastewater. Industries prefer efficient and economical treatment methods to meet challenging effluent standards regarding COD, BOD, and intensity of color. The adsorption process was optimized using low-cost adsorbents in the current study, including peanut hull and onion peel, to treat a binary mixture of acid black 48 and coffee wastewater. After adsorption, microfiltration was used to remove any suspended solids from the wastewater solution. The performance of combined treatment processes for the color removal of the binary mixture was analyzed and compared using transmittance and absorbance. Treatment efficiency of adsorption using low-cost adsorbents was compared with powdered activated carbon. Apart from absorbance and transmittance, non-purgeable organic carbon (NPOC) values were analyzed to determine organic carbon removal in the combined binary wastewater. Experimental results indicated that Langmuir isotherm was the best fit for a binary mixture with an optimum dosage of 1.2 g using onion peel. The regression coefficient value was 0.82, and the uptake was 58.13 mg of binary mixture per 1 g of onion peel. The effective pH for maximum uptake of acid black 48 using onion peel for adsorption was 5.7. The increasing dosage of low-cost adsorbents adsorption improved in removing binary waste of dyes and coffee waste from wastewater. Adsorption using onion peel improved adsorbent performance up to 1.2 g dosage and steadily decreased beyond that. The adsorption capacity of onion peel was comparatively higher than the peanut hull based on the linear fit.

Drought monitoring is crucial for water resource management in cold climate regions. For drought monitoring, different drought indices are used. Some drought indices, only consider precipitation and ignore temperature-driven evapotranspiration, which face significant limitations in cold regions. SPEI index provides a more comprehensive and adaptable tool by integrating temperature-driven evapotranspiration and water balance analysis. So, this index performance is worth evaluating in cold climate and mountainous regions. So, this study aims to compare the performance of Standardized Precipitation Evapotranspiration Index (SPEI) in two mountainous and cold climate regions in Iran and the United States (US). The Mann-Kendall Test was employed to assess the trend in the Upper Colorado River Basin (UCRB) in the US and Lorestan province. The results revealed a predominantly decreasing trend in drought occurrences across Lorestan, especially in southern and southwestern areas with lower elevations. In contrast, the UCRB showed a positive trend, indicating a wet period. The western parts of the UCRB were predominantly affected by drought. Among the studied stations, Khorram Abad station exhibited the most statistically significant trend at the 99% confidence level (Z > 2.57). The temporal trend analysis of drought revealed more positive and negative abrupt changes in the UCRB than in Lorestan. This indicates a higher degree of small-scale variability in the UCRB compared to Lorestan. This study provides a temperature-responsive method for drought assessment, supporting the development of adaptive strategies that address snowmelt variability, seasonal water availability, and shifting drought patterns in cold regions.