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 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 RESEARCH ON AIR POLLUTANTS NO2 AND SO2 IN RASEINIAI AREA USING PASSIVE SORBENTS AND BIO INDICATIVE METHODS / RASEINIŲ RAJONO ORO UŽTERŠTUMO NO2 IR SO2 TYRIMAI, TAIKANT PASYVIŲJŲ SORBENTŲ BEI BIOINDIKACINIUS METODUS

2011 ◽  
Vol 3 (5) ◽  
pp. 43-49 ◽  
Author(s):  
Neringa Mikelsonaitė ◽  
Agnė Kazlauskienė
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Environmental Protection ◽  
Research Methods ◽  
Air Pollutants ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Maple Leaves ◽  
Pollution Research

The carried out research analyzes air pollution in Raseiniai area. SO2 and NO2 exhaustion analysis is based on data for the period 2006–2009 provided by Kaunas Environmental protection department. Two air pollution research methods based on passive sorbents and bio indicators are proposed. The amount of SO2 and NO2 air pollutants is determined by conducting moss (Lichenes) tests and analyzing the fungus of maple leaves (Rhytisma acerinum). The performed research also describes the methods applied for analysis. Initial air quality monitoring using passive sorbents was carried out in the autumn of 2010. The primary results of this research revealed that the amount of analyzed air pollutants was below the permissible norm. In order to get reliable and objective results, research will be continued in the spring and summer of 2011. Santrauka Nagrinėjama oro taršos problema Raseinių rajone. Remiantis Kauno regiono aplinkos apsaugos departamento Raseinių rajono agentūros duomenimis, buvo tiriama 2006–2009 m. laikotarpio SO2 ir NO2 išmetimų kitimo tendencija. Pasiūlyti du oro taršos SO2 ir NO2 tyrimo metodai: pasyviųjų sorbentų ir boindikacijos. Tiriant bioindikacijos metodu, siūloma oro užterštumą SO2 ir NO2 teršalais vertinti atliekant kerpių (Lichenes) testą ir tiriant klevų lapų grybą (Rhytisma acerinum). Aprašyta siūlomų metodų atlikimo tvarka. Pirminiai oro kokybės stebėjimai buvo atlikti 2010 m. rudens sezonu, naudojant pasyviuosius sorbentus. Pirminiai rezultatai parodė, kad tirti teršalai neviršijo žmonių apsaugai nustatytų ribinių verčių. Pradėtus tyrimus tikslinga tęsti per 2011 m. pavasario ir vasaros sezonus, siekiant gauti objektyvius ir išsamius oro kokybės tyrimų duomenis Raseinių rajono teritorijoje.

scholarly journals A Six-year long (2013–2018) High-resolution Air Quality Reanalysis Dataset over China base on the assimilation of surface observations from CNEMC

2020 ◽  
Author(s):  
Lei Kong ◽  
Xiao Tang ◽  
Jiang Zhu ◽  
Zifa Wang ◽  
Jianjun Li ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
High Resolution ◽  
Air Pollutants ◽  
Regional Scale ◽  
Monitoring Program ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Reanalysis Dataset ◽  
Surface Observations

Abstract. Air pollution in China has changed substantially since 2013, and the effects such changes bring to the human health and environment has been an increasingly hot topic in many scientific fields. Such studies, however, are often hindered by a lack of long-term air quality dataset in China of high accuracy and spatiotemporal resolutions. In this study, a six-year long high-resolution Chinese air quality reanalysis datasets (CAQRA) has been developed by assimilating over 1000 surface air quality monitoring sites from China National Environmental Monitoring Centre (CNEMC) using the ensemble Kalman filter (EnKF) and the Nested Air Quality Prediction Modeling System (NAQPMS). Surface fields of six conventional air pollutants in China, namely PM2.5, PM10, SO2, NO2, CO and O3 for period 2013–2018, are provided at high spatial (15 km ×15 km) and temporal (1 hour) resolutions. This paper aims to document this dataset by providing the detailed descriptions of the assimilation system and presenting the first validation results for the reanalysis dataset. A five-fold cross validation (CV) method was used to assess the quality of CAQRA. The CV results show that the CAQRA has excellent performances in reproducing the magnitude and variability of the surface air pollutants in China (CV R2 = 0.52–0.81, CV RMSE = 0.54 mg/m3 for CO and 16.4–39.3 μg/m3 for other pollutants at the hourly scale). The interannual changes of the air quality in China were also well represented by CAQRA. Through the comparisons with the Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA) produced by the European Centre for Medium-Range Weather Forecasts (ECWMF) based on assimilating satellite products, we show that the CAQRA has higher accuracy in representing the surface gaseous air pollutants in China due to the assimilation of surface observations. The finer horizontal resolution of CAQRA also makes it more suitable for the air quality studies in the regional scale. We further validate the PM2.5 reanalysis dataset against the independent datasets from the U.S. Department of State Air Quality Monitoring Program over China, and the accuracy of PM2.5 reanalysis was also compared to that of the satellite estimated PM2.5 concentrations. The results indicate that the PM2.5 reanalysis shows good agreement with the independent observations (R2 = 0.74–0.86, RMSE = 16.8–33.6 μg/m3 in different cities) and its accuracy is higher than most satellite estimates. This dataset would be the first high-resolution air quality reanalysis dataset in China that can simultaneously provide the surface concentrations of six conventional air pollutants in China, which should be of great value for many studies, such as the assessment of health impacts of air pollution, investigation of the changes of air quality in China and providing training data for the statistical or AI (Artificial Intelligence) based forecast. The whole datasets are freely available at: https://doi.org/10.11922/sciencedb.00053 (Tang et al., 2020a), and a teaser product which contains the monthly and annual mean of the CAQRA has also been released at https://doi.org/10.11922/sciencedb.00092 (Tang et al., 2020b) to facilitate the potential users to download and to evaluate the improvement of CAQRA.

scholarly journals A Study Regarding the Air Pollution in Cluj-Napoca City

2011 ◽  
Vol 68 (2) ◽  
Author(s):  
Ilarie IVAN ◽  
Teodora DEAC
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Air Pollutants ◽  
Quality Monitoring ◽  
Monitoring Station ◽  
City Management ◽  
Air Quality Monitoring ◽  
Parameter Values ◽  
Modern Apparatus

In this paper, a study regarding the air pollution in Cluj-Napoca City has been conducted. Thus, the SO2, NOx, NO and NO2 parameter values were monitored using modern apparatus of air quality monitoring station, located in Cluj-Napoca city. The values were measured daily at 14 o’clock during March, 2011. Using the analyzed data, the maximum and minimum limits of air pollutants were quantified. Thus, it was assessed that the pollution is higher from Monday till Friday and it decreases during Saturday and Sunday. According to the analyzed data, a series of proposals to Cluj-Napoca city management were elaborated in order to reduce air pollution.

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>

scholarly journals THE ANALYSE OF AIR ENVIRONMENT QUALITY MONITORING IMPROVEMENT POSSIBILITIES

2015 ◽  
Vol 1 ◽  
pp. 273
Author(s):  
Gotfrīds Noviks ◽  
Andris Skromulis
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Quality Evaluation ◽  
Quality Monitoring ◽  
Dust Particles ◽  
Action Program ◽  
Air Quality Monitoring ◽  
Environment Quality ◽  
Air Quality Measurements ◽  
Quality Measurements

Paper presents the results of air pollution analyses during last 8 years in Rezekne city. There is carried out a research of atmospheric dust particles, found correlations between concentrations of different air pollutants. Is given overview about air quality measurements in other countries, pointed out air ionization importance on air quality evaluation. The aim of the research – to ground the extension of air quality monitoring indicators including parameters of the air ionisation and to work out an action program to improve an air quality in working areas and recreating zones.

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.

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