Real-time IoT-based air quality monitoring and health hazards indicator system for mines regions: a case study of Bulyanhulu gold mine

A Project Report Submitted in Partial Fulfillment of the Requirements of the Award of the Degree of Master of Science in Embedded and Mobile Systems of the Nelson Mandela African Institution of Science and TechnologyAir quality in mining regions is a significant concern due to the potential release of pollutants from mining activities and associated processes. The proximity of mining operations to communities can have detrimental effects on the air quality and pose health risks to residents. Despite the well-known harmful effects of breathing in contaminated air, yet, this concern is commonly neglected due to a lack of information regarding air quality and levels of air quality. The study indicates that the concentration of pollutants such as PM2.5/PM10, CO, CO2, SO2, and NO2 can lead to developing chronic diseases such as respiratory issues, coughs, asthma, ischemic heart diseases, and cancer; due to inhaling hazardous air. This study proposes a real- time IoT-based air quality monitoring and health hazards indicator system for mining regions. The study implements a reliable and long-range (LoRa) wireless sensing system that collects real-time air quality data and updates it to the cloud. The developed real-time IoT-based air quality monitoring system for mines region is composed of numerous sensors (MQ7, MQ135, MQ136, MiCS4514, PMS7003, DHT22), Raspberry Pi, ATmega328 microcontroller, LoRa shields, and the ThingSpeak IoT server. The system collects air pollutants such as carbon monoxide (CO), carbon dioxide (CO2), sulfur dioxide (SO2), particulate matter (PM2.5/PM10), nitrogen dioxide (NO2), temperature, and related humidity. The system is self-contained, using a solar charger shield to link a photovoltaic solar panel to a rechargeable battery for continuous operation. The smart sensing device constantly monitors air quality and uploads the results to a cloud via the coordinator node and the LoRa gateway shield, which in turn uploads the information to the ThingSpeak IoT server. The data collected are processed to calculate the Air Quality Index (AQI), which is then analyzed to generate early warnings and an indication of diseases and dangerous health hazards when exposed to such environments for a certain time. The results are displayed on a developed web-based dashboard that users can easily access and visualize the results. The system is very reliable as developed to simplify the monitoring process and provide accurate data on pollutant levels. The system helps environmental stakeholders in the air quality data aggregation, analysis, Air Quality Index (AQI) calculation, Reporting, and easy way of air quality data communication to the public as well as the indication of health hazards, allowing for informed decision-making, policy formulation, and mitigation strategies

Real-Time IoT-Based Air Quality Monitoring and Health Hazards Indicator System for Mines Regions: A Case Study of Bulyanhulu Gold Mine

This research article was published by International Journal of Computer Science and Mobile Computing Vol.12 Issue.7, July- 2023Air quality in mining regions is a significant concern due to the release of pollutants from mining activities, posing health risks to nearby communities. However, limited information on air quality levels often leads to neglect of this issue. Inhaling pollutants like PM2.5/PM10, CO, CO2, SO2, and NO2 can result in chronic diseases such as respiratory issues, asthma, and cancer. To tackle this problem, a study suggests the implementation of a real-time Internet of Things (IoT)-based air quality monitoring and health hazards indicator system for mining regions. The proposed system utilizes a reliable wireless sensing system, incorporating sensors like MQ7, MQ135, MQ136, MiCS4514, PMS7003, and DHT22, along with ESP8266, STM32, ATmega328 microcontroller, LoRa shields, and the ThingSpeak IoT server. It ensures continuous operation with a self-contained design, including a solar charger shield connected to a photovoltaic solar panel and rechargeable battery. The smart sensing device continuously monitors air quality and uploads real-time data to the cloud through a coordinator node. The collected data is processed to calculate the Air Quality Index (AQI), which is analyzed to generate early warnings and indicate potential health hazards. The results are accessible through a web-based dashboard for easy visualization. This system simplifies monitoring and provides accurate pollutant data. It supports environmental stakeholders by aggregating and analyzing air quality data, generating reports, and facilitating public access to air quality information. Additionally, it helps identify health hazards, enabling informed decision-making, policy formulation, and mitigation strategies

Development of Smart Laboratory Information Management System: A Case Study of NMAIST Arusha of Tanzania

Testing Laboratories in Higher Learning institutions of Science, Technology, and Engineering are used by institutional staff, researchers, and external stakeholders in conducting research experiments, analysis, and dissemination of results. However, there exists a challenge in the management of Laboratory operations and processing of Laboratory-based data. Operations carried out in the laboratory at Nelson Mandela African Institution of Science and Technology, in Arusha, Tanzania, where this case study was carried out, are paper-based. There is no automated way of sample registration as well as identification and researchers are prone to making errors when handling sensitive reagents. Users have to physically visit the laboratory to enquire about available equipment or reagents before borrowing or reserving. Additionally, paper-based forms have to be filled out and handed to the Laboratory Manager for approval. The outlined manual operations make it difficult to keep track of expiry dates of reagents, storage conditions such as temperature, software licenses, tools, data regarding borrowed equipment as well as stock remaining, as they lack automated notification mechanisms. This study, therefore, was carried out to investigate the Development of a Smart Laboratory Information Management System (LIMS) integrated with the Internet of Things (IoT), Wireless Sensor Network (WSN), and Radio Frequency Identification (RFID) technology for real-time monitoring of sample and reagents storage conditions, including digital sample identification and tracking respectively. A web application was developed to allow remote access to Laboratory information by users. Based on the performance test, it is concluded that Wireless Sensor Networks can be integrated with the Internet of Things to automate recurring tasks in laboratories, aid in monitoring, and eliminate paper-based record keeping