Environmental Pollution Monitoring System Based on IoT

Air pollution, climate change, and chemical exposure constitute the world's most significant environmental health concern, resulting in the early deaths of 6. 5 million people annually. Reducing child mortality from preventable causes, primarily pneumonia and other respiratory illnesses, would have contributed to the united nation's sustainable development goals (SDG). Some significant goals are sustainable cities, industry innovation, green and resilient infrastructure, good health, and well-being. Non-ventilator hospital-acquired pneumonia (NV-HAP) is a severe but preventable cause of morbidity and mortality in hospitalized patients. Despite being the most frequent and fatal hospital-acquired infection (HAI), NV-HAP is not tracked, documented, or avoided in most hospitals. The success of NV-HAP prevention and monitoring initiatives relies on reliable, up-to-date surveillance data. Surveillance offers the information needed to target, analyze, and quantify the efficacy of preventative activities by identifying patients at the highest risk for NV-HAP. However, pneumonia monitoring is complex due to the clinical criteria's subjective, imprecise, inconsistently recorded, and labor-intensive nature. Non-ventilator hospital-acquired pneumonia must be monitored and standardized, which demands cutting-edge technologies and the deployment of advanced sensors. In the framework of this research, initially, a wireless body area networks (WBANs) architecture has built by making use of wearable biosensors, and then real-time sensor data were uploaded to a cloud platform. Researchers have devised a wireless sensor network (WSN) to track volatile organic compounds (VOC) and other atmospheric characteristics in real time to curb the spread of NV-HAP. The ESP32 Internet of Things (IoT) and Raspberry Pi 4B graphical processing unit platforms host the finalized WBAN and WSN network. To reduce the mortality rate of NV-HAP, this research aims to investigate clinics' and hospitals' indoor and outdoor air quality. The developed biosensor-assisted IoT enabled framework is used in hospitals to keep tabs on the conditions of individual patient rooms, treatment areas, and critical care units in real time. The research found the suggested technique achieves better results than existing state-of-the-art methods regarding computing cost, communication overhead, storage cost, and energy utilization.

Smart waste management and air pollution forecasting: Harnessing Internet of things and fully Elman neural network.

BackgroundChildhood exposure to air pollution contributes to cardiovascular disease in adulthood. Immune and oxidative stress disturbances might mediate the effects of air pollution on the cardiovascular system, but the underlying mechanisms are poorly understood in adolescents. Therefore, we aimed to identify immune biomarkers linking air pollution exposure and blood pressure levels in adolescents.MethodsWe randomly recruited 100 adolescents (mean age, 16 years) from Fresno, California. Using central-site data, spatial-temporal modeling, and distance weighting exposures to the participant’s home, we estimated average pollutant levels [particulate matter (PM), polyaromatic hydrocarbons (PAH), ozone (O3), carbon monoxide (CO) and nitrogen oxides (NOx)]. We collected blood samples and vital signs on health visits. Using proteomic platforms, we quantitated markers of inflammation, oxidative stress, coagulation, and endothelial function. Immune cellular characterization was performed via mass cytometry (CyTOF). We investigated associations between pollutant levels, cytokines, immune cell types, and blood pressure (BP) using partial least squares (PLS) and linear regression, while adjusting for important confounders.ResultsUsing PLS, biomarkers explaining most of the variance in air pollution exposure included markers of oxidative stress (GDF-15 and myeloperoxidase), acute inflammation (C-reactive protein), hemostasis (ADAMTS, D-dimer) and immune cell types such as monocytes. Most of these biomarkers were independently associated with the air pollution levels in fully adjusted regression models. In CyTOF analyses, monocytes were enriched in participants with the highest versus the lowest PM2.5 exposure. In both PLS and linear regression, diastolic BP was independently associated with PM2.5, NO, NO2, CO and PAH456 pollution levels (P ≤ 0.009). Moreover, monocyte levels were independently related to both air pollution and diastolic BP levels (P ≤ 0.010). In in vitro cell assays, plasma of participants with high PM2.5 exposure induced endothelial dysfunction as evaluated by eNOS and ICAM-1 expression and tube formation.ConclusionsFor the first time in adolescents, we found that ambient air pollution levels were associated with oxidative stress, acute inflammation, altered hemostasis, endothelial dysfunction, monocyte enrichment and diastolic blood pressure. Our findings provide new insights on pollution-related immunological and cardiovascular disturbances and advocate preventative measures of air pollution exposure.

Background/Objectives: Owing to an enormous technological advancement, much research has been done in the communication field related to the Internet Of Things (IOT). Wireless Sensor Network devices (WSN) are the independent devices which can be used to monitor the physical and the environmental conditions for a wide array of applications across different fields. With advancement in technology and increase in population, Air pollution is the most important environmental problem which affects the human lives. Methods: LSM (Least Square method) is used to process the sensor data in order to get the accuracy. The sensors like CO₂, CO are used to detect the air pollution levels. For monitoring the air pollution constantly, the WSN nodes with Zigbee communication have been deployed. Xbees are used to transmit and receive the data from one node to another. Arduino Mega will be acting as a web server in-order to store the data into the cloud or server and will be used to connect the IP-enabled device to monitor the pollution. The values detected by the sensors will be accessed or monitored through the IP enabled Android device with an ‘Android App’. Findings: The Air pollution monitoring systems currently available now are very costly. Hence, an indoor air quality monitoring system is proposed using an Arduino and Zigbee modules, and gas sensors, which is very cost-effective. The possibility of the real-time monitoring and tracking levels of the pollutants in different time periods is also done. Improvements/Applications: This method reduces the process and observation noises and also increases the data accuracy.

Time series modeling and forecasting is an indispensable field of supervised machine learning (ML) because of its esteemed influences on several research works and real-life applications involving companies, industries, science, and engineering. Consequently, significant contributions were devoted to the development of proficient extrapolative models. On the other hand, the Internet of Things (IoT) has enhanced the surveillance of various environmental sensations, such as air pollution, through a wireless sensor network (WSN). This chapter presents an inclusive time-series predictive model that uses supervised ML techniques and the data gathered from IoT devices. The aim is to develop an artificial intelligence-IoT (AIoT) time series analytical using IoT and ML techniques in an automated and intelligent air quality-control system. A comprehensive framework of the predictive system displaying internal subsystems and modules is summarized to form a roadmap for AIoT time series model designers. This framework includes discussing alternatives and datasets for air quality data gases like the carbon monoxide (CO) collection for IoT sensors such as MQ-2. Experiments study will be conducted and reported to support the theoretical assumptions and presentation.

Materials and methods. The source of information on fertility and mortality was Russian Federal State Statis-tics Service data on 173 cities of the Russian Federation with a population of more than 100 thousand peo-ple for 2014-2016. The sources of air pollution were the Yearbooks of the state of air pollution in cities in Russia for the same period. A nonparametric analysis of variance was used to compare cities with different levels of air pollution in terms of fertility, mortality, birth-death and aging index. Results. Comparison of groups of cities with different maximum one-time levels of atmospheric pollution with benzo[a]pyrene revealed significant differences between the groups in terms of total fertility and mortal-ity rates, birth-death ratio and aging index. Comparison of groups of cities with different average annual levels of air pollution for 19 pollutants showed that only for benzo[a]pyrene, nitrogen dioxide, nitric oxide and ammonia a statistically significant relation-ship with the characteristics of population health was found. For benzo[a]pyrene, the results were similar to those obtained for the maximum one-time concentrations of benzo[a]pyrene (except for the total mortality rate, for which the relationships are not significant). Statistically significant relationships of air pollution with nitrogen dioxide, nitrogen oxide and ammonia were revealed with the total mortality rate and the birth-death ratio, and for ammonia pollution also with the demographic aging index. Conclusion. In cities of the Russian Federation, the maximum one-time air pollution with benzo[a]pyrene is statistically significantly associated with the total fertility rate, birth-death ratio and the aging index: in groups of cities with an increase in the level of atmospheric air pollution, the median values of the total fer-tility rate and the birth-death ratio are increasing, and the median values of the aging index are decreasing. Comparison of groups of cities with different average annual levels of air pollution for 19 pollutants re-vealed statistically significant effects for pollution with benzo[a]pyrene, nitrogen dioxide, nitrogen oxide and ammonia. A statistically significant heterogeneity of the groups of cities with different average annual levels of benzo[a]pyrene was revealed in terms of the total fertility rate, birth-death ratio and aging index: with an increase in the level of atmospheric air pollution, the median values of the total fertility rate and the birth-death ratio increase, while the median values of the aging index decrease. A statistically significant hetero-geneity of the groups of cities with different average annual levels of air pollution with nitrogen dioxide, ni-tric oxide and ammonia was revealed in terms of the total mortality rate and the birth-death ratio, and for ammonia pollution also in terms of the demographic aging index. In cities with a high level of air pollution with nitrogen dioxide and ammonia, the total mortality rate is higher.

Description of outdoor air quality in a typical residential area in Kuwait

Brain Fog: Does Air Pollution Make Us Less Productive?

This study was basically designed to assess the potential environmental air quality impacts arising from the existing two operational trains at the Oman Liquid Natural Gas (OLNG) plant. The results of the paper contain a baseline survey of the existing environment. The pollutants studied included methane (CH4), non-methane hydrocarbons (NMHC), carbon monoxide (CO), nitrogen oxides (NOx), and suspended particulate matters (dust PM 10). Meteorological parameters monitored simultaneously include wind speed and direction, air temperature, and relative humidity. The air quality data were used to determine the diurnal and monthly variations in the pollutants. Description levels of the pollutants with respect to meteorological data were also used in analysis. Moreover, a statistical analysis of the collected data was presented. Generally, the results indicated that the mean concentrations of pollutants were low to cause any significant impact in air quality. The area had no problem in meeting the air quality standards for CO and NO2. It was also found that there was a random relationship between CO and NMHC, and between NO and NOx (no apparent correlation). The diurnal peaks of NOx, NO2, THC, and NMHC over a 24-h period were observed at around 9:00–10:00 AM (morning peak). For NO, NO2, and NOx, another peak was seen at around 5:00 PM (evening peak). Furthermore, the measured concentrations for NO2, NOx, and CO were found higher in winter than in summer. The study would help to gain a better understanding of local background levels of air pollutants at the area prior to the construction of new industrial projects, and to prepare action plans for controlling pollution in the area.

The Internet of things (IoT) and wireless sensor networks (WSNs) have been rapidly and tremendously developing recently as computing technologies have brought about a significant revolution. Their applications and implementations can be found all around us, either individually or collaboratively. WSN plays a leading role in developing the general flexibility of industrial resources in terms of increasing productivity in the IoT. The critical principle of the IoT is to make existing businesses sufficiently intelligent to recognize the need for significant fault mitigation and short-cycle adaptation to improve effectiveness and financial profits. This article presents efficiently applied security protocols at the data link layer for WSN and IoT-based frameworks. It outlines the importance of WSN–IoT applications as well as the architecture of WSN in the IoT. Our primary aim is to highlight the research issues and limitations of WSNs related to the IoT. The fundamental goal of this work is to emphasize a suggested architecture linked to WSN–IoT to enhance energy and power consumption, mobility, information transmission, QoS, and security, as well as to present practical solutions to data link layer difficulties for the future using machine learning. Moreover, we present data link layer protocol issues, attacks, limitations, and research gaps for WSN frameworks based on the recent work conducted on the data link layer concerning WSN applications. Current significant issues and challenges pertain to flow control, quality of service (QoS), security, and performance. In the context of the literature, less work has been undertaken concerning the data link layer in WSN and its relation to improved network performance.

WITHDRAWN: Prevention and control of motor vehicle exhaust pollution based on internet of things system and cloud computing

Herein, an Internet of Things (IoT) system with remote reconfigurable wireless sensor network (WSN) nodes is presented and its application to measure group work activity in a classroom is discussed. This IoT system consists of wiki pages, bots, (defined as remotely controlled devices), gateways, and remotely reconfigurable WSN Nodes. The bots/gateways and WSN nodes of the IoT system are controlled by scripts written on wiki pages that are read by the bot/gateways, which then forward the commands contained in the scripts to the WSN nodes. Each WSN node then executes the commands, obtains the resulting sensor values, and then returns those values to the bot/gateways. The bot/gateways relay the values and write them onto the wiki page. Each WSN node is flexible and reconfigurable because its behavior can be changed by the script commands written onto the wiki page. Using this IoT system, we created a measurement system to evaluate activity levels of group work in a classroom. After developing the measurement system, we found it was possible to adjust the sampling rate for obtaining sensor values simply by rewriting the script on the wiki page without directly manipulating the WSN.

Urban residential energy switching in China between 1980 and 2014 prevents 2.2 million premature deaths

With the development of Internet-of-Things (IoT) technologies, a wide range of Wireless Sensor Networks (WSNs) will be adopted in a variety of application environments. Given the fact that most of these WSNs are constrained by limitations in energy resources, processing capability, storage capacity, and communication range, their deployment must satisfy the real-time constraints based on the type of applications. This paper proposes a real-time IoT-based WSN with configurable nodes with multiple sensors. The nodes can collect data from the surrounding environment, and present it on the cloud for universal accessibility by the users. With this system, users can easily access and analyze various data, such as temperature, humidity, motion detection, illuminance, CO gas, air quality, etc. and take necessary actions upon that information. The feasibility of proposed WSN IoT framework has been verified through real-time hardware implementation.

The study aimed to monitor air quality seen from the aspects of pH, temperature, and turbidity levels in shrimp ponds to increase the production of shrimp farmers. The study used Research and Development (R&D) method with a prototype development model. The subjects in this study were shrimp farmers in Pangkep Regency, South Sulawesi. It produced a prototype of an Internet Of Things (IoT)-based air quality monitoring tool that used a pH sensor, temperature sensor, turbidity sensor as an air quality detector. This product used an Esp32 microcontroller as a storage for air quality monitoring data and the Blynk application as a real-time information sender. The results of the functionality test showed that the tool was very feasible to use (100%) and the usability test by respondents showed that the tool developed was very good.