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 Analysis of short-term air quality monitoring data in a coastal area

2021 ◽  
Vol 8 (6) ◽  
pp. 517-531
Author(s):  
Suwimon Kanchanasuta ◽  
◽  
Sirapong Sooktawee ◽  
Natthaya Bunplod ◽  
Aduldech Patpai ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Coastal Area ◽  
Source Area ◽  
Quality Monitoring ◽  
Monitoring Data ◽  
World Health ◽  
Air Quality Monitoring ◽  
Short Term ◽  
Potential Source

<abstract> <p>Short-term air quality monitoring in a coastal area, Naklua Subdistrict, Pattaya, Thailand is an activity to support the designated area under Thailand's sustainable tourism development. This study provided a short-term monitoring data analysis on time series and Bivariate Polar Plot (BVP) to provide the status of air quality and to determine the potential source area of air pollution. The result showed that NO<sub>2</sub>, SO<sub>2</sub>, CO and PM<sub>10</sub> were not higher than the national air quality standards, while the 24-hour average of PM<sub>2.5</sub> and the 8-hour average of O<sub>3</sub> were slightly higher than the World Health Organization (WHO) air quality guideline values. The nighttime PM<sub>2.5</sub> concentration was higher than the daytime concentration, and its potential source area is urban areas in the south. However, the daytime O<sub>3</sub> concentration is higher than the nighttime concentration. Its potential source area is from the northwest, where Sichang island is located. This result could be used to support air pollution management by controlling and reducing emissions in the potential source areas as the first priority. Also, the study revealed that the BVP technique could be used to determine the source area of air pollution in the coastal area, where wind circulation is more complex than that over the land.</p> </abstract>

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 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 ANALYSIS OF THE RESULTS OF AIR QUALITY MONITORING IN THE AREA OF THE CITY OF ZENICA

2021 ◽  
Vol 9 (12) ◽  
pp. 453-461
Author(s):  
Mirnes Durakovic ◽  
◽  
Azrudin Husika ◽  
Halim Prcanovic ◽  
Sanela Beganovic ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Sulfur Dioxide ◽  
Sampling Period ◽  
Ambient Air ◽  
Quality Monitoring ◽  
World Health ◽  
Air Quality Monitoring ◽  
Short Period ◽  
The City

According to the World Health Organization (WHO), air pollution is the largest single environmental risk to public health. According to the latest estimate of this organization, 9 out of 10 people on the planet breathe polluted air. The development of industry in the relatively small Zenica valley reflected on air quality in the city of Zenica. The problem of high air pollution due to emissions of pollutants from industrial sources, traffic, and individual furnaces, burning of environmentally unsuitable fuels containing high sulfur and ash content has been present in the City of Zenica for a long time. In addition, the low wind speed during the year, which ranges up to 1.5 m/s, with unfavorable temperature inversions, causes the concentrations of pollutants in the air to reach alarmingly high values in a short period. In the wider area of the City of Zenica, air quality has been monitored since 1978 in the network of stationary stations. The paper presents results of air quality monitoring which are analyzed at the Institute Kemal Kapetanovic in Zenica for the sampling period from 01.01.2019. to 31.12.2020. years. Air quality monitoring included sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and particulate matter (PM10) at three locations in the wider area of the city of Zenica. In the wider area of the City of Zenica, air quality has been monitored since 1978 in the network of stationary stations. The paper presents the processed results of air quality monitoring which are analyzed at the Institute Kemal Kapetanovic in Zenica for the sampling period from 01.01.2019 to 31.12.2020. The measured concentrations of pollutants in the ambient air indicate that during the heating season, i.e. the winter months, the air quality in the urban and suburban areas of the city of Zenica is very poor. The data show that the highest hourly concentration of sulfur dioxide was recorded in December at the measuring station AMS Tetovo in the amount of 1100.59 µg/m3, which is located in the settlement next to the metallurgical facilities of the industrial zone Zenica.

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 Advances in Air Quality Monitoring and Assessment

2021 ◽  
Vol 11 (13) ◽  
pp. 5817
Author(s):  
Thomas Maggos
Keyword(s):  
Air Quality ◽  
Quality Monitoring ◽  
Air Quality Monitoring

Air quality monitoring is a long-term assessment of pollutant levels that helps to assess the extent of pollution and provide information about air quality trends [...]

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