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.

Perovskite solar cells integrated with lower dimensional materials can outperform the environmental performance of conventional solar photovoltaic technologies such as crystalline silicon, CIGS, and CdTe with shorter lifetimes.

The transportation industry has led efforts to fight climate change and reduce air pollution. Autonomous electric vehicles (A-EVs) that use artificial intelligence, next-generation batteries, etc., are predicted to replace conventional internal combustion engine vehicles (ICEVs) and electric vehicles (EVs) in the coming years. In this study, we performed a life cycle assessment to analyze A-EVs and compare their impacts with those from EV and ICEV systems. The scope of the analysis consists of the manufacturing and use phases, and a functional unit of 150,000 miles·passenger was chosen for the assessment. Our results on the impacts from the manufacturing phase of the analyzed systems show that the A-EV systems have higher impacts than other transportation systems in the majority of the impacts categories analyzed (e.g., global warming potential, ozone depletion, human toxicity-cancer) and, on average, EV systems were found to be the slightly more environmentally friendly than ICEV systems. The high impacts in A-EV are due to additional components such as cameras, sonar, and radar. In comparing the impacts from the use phase, we also analyzed the impact of automation and found that the use phase impacts of A-EVs outperform EV and ICEV in many aspects, including global warming potential, acidification, and smog formation. To interpret the results better, we also investigated the impacts of electricity grids on the use phase impact of alternative transportation options for three representative countries with different combinations of renewable and conventional primary energy resources such as hydroelectric, nuclear, and coal. The results revealed that A-EVs used in regions that have hydropower-based electric mix become the most environmentally friendly transportation option than others.