scholarly journals EVALUATION OF PARTICULATE MATTER (PM10) DISTRIBUTIONS IN İZMIR USING GEOGRAPHIC INFORMATION SYSTEMS FOR SMART CITIES APPLICATIONS

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 545-555
Author(s):  
K. Ulutaş ◽  
S. K. M. Abujayyab ◽  
İ. R. Karaş
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Smart Cities ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Distribution Map ◽  
City Managers ◽  
Limit Values ◽  
Limit Value ◽  
Different Sources

Abstract. In this study, PM10 values from the air quality monitoring station in Izmir was evaluated. 9 stations could be used in this study, since PM10 data are suitable to evaluate for the years 2020-2019-2018. The 4-season and annual PM10 distribution map for 3 years was prepared using ArcGIS. The benefits of these maps to city managers in the smart city application were expressed. In addition, PM10 data of 9 stations were evaluated according to legal limit values. It was determined that Aliağa and Gaziemir stations exceeded the limit values more than other stations. It has been observed that different sources of air pollution such as industry, traffic and heating affect different districts. When the number of days exceeding the limit value and the number of days without measurement are evaluated together, it is seen that the limit values are exceeded by all stations.

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 & 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.

Air Quality Monitoring Using Internet of Things (IoT) in Smart Cities

2019 ◽  
pp. 317-333
Author(s):  
Gayatri Doctor ◽  
Payal Patel
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Internet Of Things ◽  
Environmental Monitoring ◽  
Real Time ◽  
Smart Cities ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Monitoring Applications ◽  
Environmental Health Problem

Air pollution is a major environmental health problem affecting everyone. An air quality index (AQI) helps disseminate air quality information (almost in real time) about pollutants like PM10, PM2.5, NO2, SO2, CO, O3, etc. In the 2018 environmental performance index (EPI), India ranks 177 out of 180 countries, which indicates a need for awareness about air pollution and air quality monitoring. Out of the 100 smart cities in the Indian Smart City Mission, which is an urban renewal program, many cities have considered the inclusion of smart environment sensors or smart poles with environment sensors as part of their proposals. Internet of things (IoT) environmental monitoring applications can monitor (in near real time) the quality of the air in crowded areas, parks, or any location in the city, and its data can be made publicly available to citizens. The chapter describes some IoT environmental monitoring applications being implemented in some of the smart cities like Surat, Kakinada.

scholarly journals One-year simulation of ozone and particulate matter in China using WRF/CMAQ modeling system

2016 ◽  
Vol 16 (16) ◽  
pp. 10333-10350 ◽  
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 an 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 and Forecasting (WRF) model and the Community Multi-scale Air Quality (CMAQ) model was conducted to provide detailed temporal and spatial information of ozone (O3), total PM2.5, 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, overprediction of O3 generally occurs at low concentration range while underprediction of PM2.5 happens at low concentration range in summer. Spatially, the model has better performance in southern China than in northern China, central China, 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 the CMAQ model to reproduce 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.

Time Series Analysis for Prediction of PM2.5 Using Seasonal Autoregressive Integrated Moving Average (SARIMA) Model on Taiwan Air Quality Monitoring Network Data

2020 ◽  
Vol 17 (9) ◽  
pp. 3964-3969
Author(s):  
Doreswamy ◽  
K. S. Harish Kumar ◽  
Ibrahim Gad
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Moving Average ◽  
Monitoring Network ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Data Set ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average

Nowadays, in Taiwan, due to the increasing number of vehicles, industrialization of large energy consumption, uncontrolled constructions and urbanization, air pollution is becoming a major problem. Hence, it is necessary to control air pollution by applying air quality monitoring actions. The particulate matter (PM2.5) of the air pollution in TAQMN data is the main pollutant accountable for at least two-thirds of the severely polluted days in the major cities of Taiwan. In this work, machine learning (ML) techniques are widely used in developing models that can be used to control the air pollution. Seasonal Autoregressive Integrated Moving Average (SARIMA) model is used to predict the air pollution concentration, where the dataset chronologically from 2012 to 2016 are used to train the proposed method and testing data set from 2016 to 2017. The result of the SARIMA model shows high precision in forecasting the future values of particulate matter (P2.5) level with minimum error.

scholarly journals Novel Air Pollution Measurement System Based on Ethereum Blockchain

2020 ◽  
Vol 9 (4) ◽  
pp. 49
Author(s):  
Daniele Sofia ◽  
Nicoletta Lotrecchiano ◽  
Paolo Trucillo ◽  
Aristide Giuliano ◽  
Luigi Terrone
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Query Language ◽  
Environmental Data ◽  
Quality Monitoring ◽  
Traffic Information ◽  
Quality Data ◽  
Air Quality Monitoring ◽  
Monitoring Networks

The need to protect sensitive data is growing, and environmental data are now considered sensitive. The application of last-generation procedures such as blockchains coupled with the implementation of new air quality monitoring technology allows the data protection and validation. In this work, the use of a blockchain applied to air pollution data is proposed. A blockchain procedure has been designed and tested. An Internet of Things (IoT)-based sensor network provides air quality data in terms of particulate matter of two different diameters, particulate matter (PM)10 and PM2.5, volatile organic compounds (VOC), and nitrogen dioxide (NO2) concentrations. The dataset also includes meteorological parameters and vehicular traffic information. This work foresees that the data, recovered from traditional Not Structured Query Language (NoSQL) database, and organized according to some specifications, are sent to the Ethereum blockchain daily automatically and with the possibility to choose the period of interest manually. There was also the development of a transaction management and recovery system aimed at retrieving data, formatting it according to the specifications and organizing it into files of various formats. The blockchain procedure has therefore been used to track data provided by air quality monitoring networks unequivocally.

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 Analyzing and Improving the Performance of a Particulate Matter Low Cost Air Quality Monitoring Device

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 251
Author(s):  
Evangelos Bagkis ◽  
Theodosios Kassandros ◽  
Marinos Karteris ◽  
Apostolos Karteris ◽  
Kostas Karatzas
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Urban Areas ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Quality Monitoring ◽  
Reference Station ◽  
Air Quality Monitoring ◽  
Pollution Levels

Air quality (AQ) in urban areas is deteriorating, thus having negative effects on people’s everyday lives. Official air quality monitoring stations provide the most reliable information, but do not always depict air pollution levels at scales reflecting human activities. They also have a high cost and therefore are limited in number. This issue can be addressed by deploying low cost AQ monitoring devices (LCAQMD), though their measurements are of far lower quality. In this paper we study the correlation of air pollution levels reported by such a device and by a reference station for particulate matter, ozone and nitrogen dioxide in Thessaloniki, Greece. On this basis, a corrective factor is modeled via seven machine learning algorithms in order to improve the quality of measurements for the LCAQMD against reference stations, thus leading to its on-field computational improvement. We show that our computational intelligence approach can improve the performance of such a device for PM10 under operational conditions.

scholarly journals Air Quality Monitoring and Effect of Particulate Air Pollution on Respiratory Health in the population of Raiganj, West Bengal, India.

2021 ◽  
Author(s):  
Debraj Mukhopadhyay ◽  
J. Swaminathan ◽  
Arun Sharma ◽  
Soham Basu
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Small Town ◽  
West Bengal ◽  
Quality Monitoring ◽  
World Health ◽  
Air Quality Monitoring ◽  
Short Term

Abstract According to The World Health Organization (WHO) reports air pollution from particulate matter (PM), which ranks 13th highest worldwide in terms of mortality, causes about 800,000 premature deaths a year. However several finding demonstrated that the correlation is stronger than initially believed and much more complex. PM is an air emission component composed of very minute, acid, organic compounds, metals, and particulate soil or dust-containing fragments or fluid droplets. PM is classified by size and remains the most reliable part of the air pollution linked to human disease. The processes of systemic inflammation, overt and indirect coagulation activation and direct translocation to systemic circulation are expected to lead PM to cardiovascular and cerebrovascular diseases. Data on the cardiovascular system that show a PM effect are strong. The coronary incident and death rates of communities exposed to long-term exposure to PM was considerably higher. The rate of coronary incidents within days of the emission high is raised subtly by short-term acute exposures. The results are not as good for PM's cortical disease effects, although some data and related pathways indicate a smaller outcome. Exposure of PM is also an aggravation of respiratory diseases. During more research in order to understand the implications for disadvantaged populations in structure, chemistry, and PM, the prevalent evidence suggests that PM exposure results in a minor but substantial rise in human morbidity and mortality. The use of air conditioning and filters for particulate matter decreased internal heating and cooking combustion and smoking stoppage will minimize the indoor PM exposure. These basic improvements could be useful to individual patients in both short-term and long-term cardiovascular and respiratory symptoms. However there is very limited data available on the status of air pollution in non metropolitan cities and even less in small towns across the country. Raiganj is a small town across the country. Raiganj is a small town and the district head quarter of Uttar Dinajpur district in West Bengal. It is located at N25.6266428, E87.8012599 coordinates. To the best of our knowledge, no air quality monitoring is being done in this town. Neither any study has been conducted on the residents of this town to find out the effect of air pollution on their health. In this study we examine the overall effects of a series of new air quality regulations that have differentially affected air quality in Raiganj, relative to its outlying areas.

scholarly journals A Study on Indoor Particulate Matter Variation in Time Based on Count and Sizes and in Relation to Meteorological Conditions

2021 ◽  
Vol 13 (15) ◽  
pp. 8263
Author(s):  
Marius Bodor
Keyword(s):  
Particulate Matter ◽  
Meteorological Data ◽  
Monitoring Network ◽  
Pollution Monitoring ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Indoor Location ◽  
Particle Matter ◽  
Indoor Particulate Matter

An important aspect of air pollution analysis consists of the varied presence of particulate matter in analyzed air samples. In this respect, the present work aims to present a case study regarding the evolution in time of quantified particulate matter of different sizes. This study is based on data acquisitioned in an indoor location, already used in a former particulate matter-related article; thus, it can be considered as a continuation of that study, with the general aim to demonstrate the necessity to expand the existing network for pollution monitoring. Besides particle matter quantification, a correlation of the obtained results is also presented against meteorological data acquisitioned by the National Air Quality Monitoring Network. The transformation of quantified PM data in mass per volume and a comparison with other results are also addressed.

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