scholarly journals Air Pollution Prediction using IOT

2020 ◽  
Vol 9 (1) ◽  
pp. 1127-1130
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Environmental Conditions ◽  
Quality Monitoring ◽  
Major Constituent ◽  
Monitoring Systems ◽  
Air Quality Monitoring ◽  
Android Application ◽  
Pollutant Gases ◽  
Air Pollution Prediction

A model for Greenhouse farming Air PollutionMonitoring System for the concentrations of majorair pollutant gases has been developed. Air pollution in greenhouse farming is caused due to various elements in which the major constituent are fertilizers.This system uses IOT based Air Quality monitoring system and prediction framework.This device measures the concentration of harmfull gases like CO,LPG,Ammonia,Methane using gas sensors.The sensors will detect the environmental conditions of the farm and will send the data to Arduino.The device is connected to Cloud using ThingSpeak via WiFi module.This system provides information about the air quality in the farm with the help of Android application which helps the farmers to maintain the air conditions in the farming land.The motivation of this project is to provide a cheaper monitoring systems which is beneficiary to all users

scholarly journals Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5448 ◽  
Author(s):  
Sharnil Pandya ◽  
Hemant Ghayvat ◽  
Anirban Sur ◽  
Muhammad Awais ◽  
Ketan Kotecha ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Linear Regression ◽  
Air Pollutants ◽  
Weather Prediction ◽  
Quality Monitoring ◽  
Prediction System ◽  
Air Quality Monitoring ◽  
Air Pollution Prediction ◽  
Pollution Prediction

Air pollution has been a looming issue of the 21st century that has also significantly impacted the surrounding environment and societal health. Recently, previous studies have conducted extensive research on air pollution and air quality monitoring. Despite this, the fields of air pollution and air quality monitoring remain plagued with unsolved problems. In this study, the Pollution Weather Prediction System (PWP) is proposed to perform air pollution prediction for outdoor sites for various pollution parameters. In the presented research work, we introduced a PWP system configured with pollution-sensing units, such as SDS021, MQ07-CO, NO2-B43F, and Aeroqual Ozone (O3). These sensing units were utilized to collect and measure various pollutant levels, such as PM2.5, PM10, CO, NO2, and O3, for 90 days at Symbiosis International University, Pune, Maharashtra, India. The data collection was carried out between the duration of December 2019 to February 2020 during the winter. The investigation results validate the success of the presented PWP system. In the conducted experiments, linear regression and artificial neural network (ANN)-based AQI (air quality index) predictions were performed. Furthermore, the presented study also found that the customized linear regression methodology outperformed other machine-learning methods, such as linear, ridge, Lasso, Bayes, Huber, Lars, Lasso-lars, stochastic gradient descent (SGD), and ElasticNet regression methodologies, and the customized ANN regression methodology used in the conducted experiments. The overall AQI values of the air pollutants were calculated based on the summation of the AQI values of all the presented air pollutants. In the end, the web and mobile interfaces were developed to display air pollution prediction values of a variety of air pollutants.

scholarly journals IoT-based air quality monitoring systems for smart cities: A systematic mapping study

2021 ◽  
Vol 11 (4) ◽  
pp. 3470
Author(s):  
Danny Munera ◽  
Diana P. Tobon V. ◽  
Johnny Aguirre ◽  
Natalia Gaviria Gomez
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Smart Cities ◽  
Pollution Monitoring ◽  
Quality Monitoring ◽  
Monitoring Systems ◽  
Systematic Mapping Study ◽  
Air Quality Monitoring ◽  
Mapping Study ◽  
Systematic Mapping

<p>The increased level of air pollution in big cities has become a major concern for several organizations and authorities because of the risk it represents to human health. In this context, the technology has become a very useful tool in the contamination monitoring and the possible mitigation of its impact. Particularly, there are different proposals using the internet of things (IoT) paradigm that use interconnected sensors in order to measure different pollutants. In this paper, we develop a systematic mapping study defined by a five-step methodology to identify and analyze the research status in terms of IoT-based air pollution monitoring systems for smart cities. The study includes 55 proposals, some of which have been implemented in a real environment. We analyze and compare these proposals in terms of different parameters defined in the mapping and highlight some challenges for air quality monitoring systems implementation into the smart city context.</p>

scholarly journals Domain adaptation based deep calibration of low-cost PM2.5 sensors

2021 ◽  
Author(s):  
Sonu Kumar Jha ◽  
Mohit Kumar ◽  
Vipul Arora ◽  
Sachchida Nand Tripathi ◽  
Vidyanand Motiram Motghare ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Domain Adaptation ◽  
Low Cost ◽  
Monitoring Network ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Proposed Model ◽  
Temperature And Humidity ◽  
Calibration Approach

<div>Air pollution is a severe problem growing over time. A dense air-quality monitoring network is needed to update the people regarding the air pollution status in cities. A low-cost sensor device (LCSD) based dense air-quality monitoring network is more viable than continuous ambient air quality monitoring stations (CAAQMS). An in-field calibration approach is needed to improve agreements of the LCSDs to CAAQMS. The present work aims to propose a calibration method for PM2.5 using domain adaptation technique to reduce the collocation duration of LCSDs and CAAQMS. A novel calibration approach is proposed in this work for the measured PM2.5 levels of LCSDs. The dataset used for the experimentation consists of PM2.5 values and other parameters (PM10, temperature, and humidity) at hourly duration over a period of three months data. We propose new features, by combining PM2.5, PM10, temperature, and humidity, that significantly improved the performance of calibration. Further, the calibration model is adapted to the target location for a new LCSD with a collocation time of two days. The proposed model shows high correlation coefficient values (R2) and significantly low mean absolute percentage error (MAPE) than that of other baseline models. Thus, the proposed model helps in reducing the collocation time while maintaining high calibration performance.</div>

Indoor Air Quality Monitoring Systems

2021 ◽  
Vol 11 (3) ◽  
pp. 1-14
Author(s):  
Rasha AbdulWahhab ◽  
Karan Jetly Jetly ◽  
Shqran Shakir
Keyword(s):  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Ad Hoc ◽  
Quality Monitoring ◽  
Monitoring Systems ◽  
Air Quality Monitoring ◽  
Research Activity ◽  
Pollution Levels ◽  
Closed Area

Research activity in the field of monitoring indoor quality systems has increased dramatically in recent years. Monitoring closed areas can reduce health-related risks due to poor or contaminated air quality. In the current COVID pandemic, the population has observed that improving ventilation in the closed area can significantly reduce infection risk. However, the significance of air quality statistics makes highly accurate real-time monitoring systems vital. In this paper, several researchers' protocols and the methodologies for monitoring a good high indoor air quality system are presented. The majority of the reviewed works are aimed to reduce air pollution levels of the atmosphere. The vast majority of the identified works utilized IoT and WSN technology to fix the partial access to sensed data, high cost, and non-scalability of conventional air monitoring systems. Furthermore, ad-hoc approaches are predominantly used to help society change its attitude and impose corrective actions to improve air quality. This paper presents a short but comprehensive review of several researchers works with different approaches to ecological trend analysis capabilities, drawing on existing literature works. Overall, the findings highlight the need for developing systematic protocols for these systems and establishing smart air quality monitoring systems capable of measuring pollutant concentrations in the air.

An assessment of ambient air quality using AQI and exceedance factor for Udaipur City, Rajasthan (India)

2021 ◽  
pp. 94-106
Author(s):  
Porush Kumar ◽  
Kuldeep ◽  
Nilima Gautam
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Pollution Control ◽  
Urban Air Pollution ◽  
Ambient Air ◽  
Quality Monitoring ◽  
Urban Air ◽  
Air Quality Monitoring ◽  
Ambient Air Quality ◽  
Control Board

Air pollution is a severe issue of concern worldwide due to its most significant environmental risk to human health today. All substances that appear in excessive amounts in the environment, such as PM10, NO2, or SO2, may be associated with severe health problems. Anthropogenic sources of these pollutants are mainly responsible for the deterioration of urban air quality. These sources include stationary point sources, mobile sources, waste disposal landfills, open burning, and similar others. Due to these pollutants, people are at increased risk of various serious diseases like breathing problems and heart disease, and the death rate due to these diseases can also increase. Hence, air quality monitoring is essential in urban areas to control and regulate the emission of these pollutants to reduce the health impacts on human beings. Udaipur has been selected for the assessment of air quality with monitored air quality data. Air quality monitoring stations in Udaipur city are operated by the CPCB (Central Pollution Control Board) and RSPCB (Rajasthan State Pollution Control Board). The purpose of this study is to characterize the level of urban air pollution through the measurement of PM10, NO2, or SO2 in Udaipur city, Rajasthan (India). Four sampling locations were selected for Udaipur city to assess the effect of urban air pollution and ambient air quality, and it was monitored for a year from 1st January 2019 to 31st December 2019. The air quality index has been calculated with measured values of PM10, NO2, and SO2. The concentration of PM10 is at a critical level of pollution and primarily responsible for bad air quality and high air quality Index in Udaipur city.

scholarly journals Healthier breath for all and everywhere – Urban air quality monitoring experience

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
J Gajic ◽  
D Dimovski ◽  
B Vukajlovic ◽  
M Jevtic
Keyword(s):  
Public Health ◽  
Air Pollution ◽  
Air Quality ◽  
Health Risk ◽  
Urban Health ◽  
Urban Areas ◽  
Danube River ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Residential Areas

Abstract Issue/problem Increasing attention is being paid to air pollution as one of the greatest threats to public and urban health. The WHO’s Urban Health Initiative points out the importance of collecting data and mapping the present state of air quality in urban areas. For citizens, such engagement is enabled by the appearance of personal air quality measurement devices that use crowd-sourcing to make measurement results publicly accessible in real time. Description of the problem As a way of contributing to air pollution monitoring in their town, three PhD Public health students conducted over 40 measurements between the start of June and end of August 2018 on various locations in the city of Novi Sad, Serbia. Measurements were performed using AirBeam personal air quality monitoring devices and their results presented as μg/m3 of Particulate Matter 2.5 (PM2.5) and automatically uploaded to the internet using the Air-casting app. Results Measurements conducted in public transportation vehicles returned the rather high average value of 40 μg/m3, where coffee shops and restaurants scored an even higher value of 48,67 μg/m3. The lowest average air pollution levels were registered near the Danube river bank (5.67) and in the parks (6), while the sites near crossroads or in the street showed average air pollution of 8.33 μg/m3. Residential areas where smoking is present during the day reported 2.5 times higher PM2.5 values than those without smokers (33.8 and 12.78 μg/m3). Lessons Bearing in mind that the air quality is considered as a serious health risk in urban areas, results of this pilot investigation suggest potential health risk for citizens living in urban areas. The negative effects of combustion and smoking on air quality are strongly highlighted, as well as the positive impact of green areas and parks near residential areas. Key messages Air pollution exposure as a serious health risk in urban areas. Crowdsourcing as a way of air quality monitoring has great potential for contributing to public health.

scholarly journals One-Year Simulation of Ozone and Particulate Matter in China Using WRF/CMAQ Modeling System

2016 ◽  
Author(s):  
Jianlin Hu ◽  
Jianjun Chen ◽  
Qi Ying ◽  
Hongliang Zhang
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Model Performance ◽  
Monitoring Network ◽  
Organic Aerosol ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Low Concentration ◽  
Temporal And Spatial

Abstract. China has been experiencing severe air pollution in recent decades. Although ambient air quality monitoring network for criteria pollutants has been constructed in over 100 cities since 2013 in China, the temporal and spatial characteristics of some important pollutants, such as particulate matter (PM) components, remain unknown, limiting further studies investigating potential air pollution control strategies to improve air quality and associating human health outcomes with air pollution exposure. In this study, a yearlong (2013) air quality simulation using the Weather Research &amp; Forecasting model (WRF) and the Community Multi-scale Air Quality model (CMAQ) was conducted to provide detailed temporal and spatial information of ozone (O3), PM2.5 total and chemical components. Multi-resolution Emission Inventory for China (MEIC) was used for anthropogenic emissions and observation data obtained from the national air quality monitoring network were collected to validate model performance. The model successfully reproduces the O3 and PM2.5 concentrations at most cities for most months, with model performance statistics meeting the performance criteria. However, over-prediction of O3 generally occurs at low concentration range while under-prediction of PM2.5 happens at low concentration range in summer. Spatially, the model has better performance in Southern China than in Northern, Central and Sichuan basin. Strong seasonal variations of PM2.5 exist and wind speed and direction play important roles in high PM2.5 events. Secondary components have more boarder distribution than primary components. Sulfate (SO42−), nitrate (NO3−), ammonium (NH4+), and primary organic aerosol (POA) are the most important PM2.5 components. All components have the highest concentrations in winter except secondary organic aerosol (SOA). This study proves the ability of CMAQ model in reproducing severe air pollution in China, identifies the directions where improvements are needed, and provides information for human exposure to multiple pollutants for assessing health effects.

scholarly journals Edge Computing Based IoT Architecture for Low Cost Air Pollution Monitoring Systems: A Comprehensive System Analysis, Design Considerations & Development

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3021 ◽  
Author(s):  
Zeba Idrees ◽  
Zhuo Zou ◽  
Lirong Zheng
Keyword(s):  
Air Quality ◽  
Power Consumption ◽  
Real Time ◽  
Low Cost ◽  
Edge Computing ◽  
Quality Monitoring ◽  
Quality Data ◽  
Monitoring Systems ◽  
Air Quality Monitoring ◽  
Computing Device

With the swift growth in commerce and transportation in the modern civilization, much attention has been paid to air quality monitoring, however existing monitoring systems are unable to provide sufficient spatial and temporal resolutions of the data with cost efficient and real time solutions. In this paper we have investigated the issues, infrastructure, computational complexity, and procedures of designing and implementing real-time air quality monitoring systems. To daze the defects of the existing monitoring systems and to decrease the overall cost, this paper devised a novel approach to implement the air quality monitoring system, employing the edge-computing based Internet-of-Things (IoT). In the proposed method, sensors gather the air quality data in real time and transmit it to the edge computing device that performs necessary processing and analysis. The complete infrastructure & prototype for evaluation is developed over the Arduino board and IBM Watson IoT platform. Our model is structured in such a way that it reduces the computational burden over sensing nodes (reduced to 70%) that is battery powered and balanced it with edge computing device that has its local data base and can be powered up directly as it is deployed indoor. Algorithms were employed to avoid temporary errors in low cost sensor, and to manage cross sensitivity problems. Automatic calibration is set up to ensure the accuracy of the sensors reporting, hence achieving data accuracy around 75–80% under different circumstances. In addition, a data transmission strategy is applied to minimize the redundant network traffic and power consumption. Our model acquires a power consumption reduction up to 23% with a significant low cost. Experimental evaluations were performed under different scenarios to validate the system’s effectiveness.

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