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.

scholarly journals Air Quality Measurements in Kitchener, Ontario, Canada Using Multisensor Mini Monitoring Stations

Atmosphere ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 83
Author(s):  
Wisam Mohammed ◽  
Nicole Shantz ◽  
Lucas Neil ◽  
Tom Townend ◽  
Adrian Adamescu ◽  
...  
Keyword(s):  
Air Quality ◽  
Quality Monitoring ◽  
Monitoring Station ◽  
Air Quality Monitoring ◽  
Long Distance ◽  
Air Quality Measurements ◽  
Custom Made ◽  
The Impact ◽  
Quality Measurements ◽  
Monitoring Stations

The Region of Waterloo is the third fastest growing region in Southern Ontario in Canada with a population of 619,000 as of 2019. However, only one air quality monitoring station, located in a city park in Kitchener, Ontario, is currently being used to assess the air quality of the region. In September 2020, a network of AQMesh Multisensor Mini Monitoring Stations (pods) were installed near elementary schools in Kitchener located near different types of emission source. Data analysis using a custom-made long-distance scaling software showed that the levels of nitrogen oxides (NO and NO2), ground level ozone (O3), and fine particulate matter (PM2.5) were traffic related. These pollutants were used to calculate the Air Quality Health Index-Plus (AQHI+) at each location, highlighting the inability of the provincial air quality monitoring station to detect hotspot areas in the city. The case study presented here quantified the impact of the 2021 summer wildfires on the local air quality at a high time resolution (15-min). The findings in this article show that these multisensor pods are a viable alternative to expensive research-grade equipment. The results highlight the need for networks of local scale air quality measurements, particularly in fast-growing cities in Canada.

scholarly journals Air Pollutants Prediction in Shenzhen Based on ARIMA and Prophet Method

2019 ◽  
Vol 136 ◽  
pp. 05001 ◽  
Author(s):  
Ziyuan Ye
Keyword(s):  
Air Quality ◽  
Hybrid Model ◽  
Air Pollutants ◽  
Moving Average ◽  
Mixing Time ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Autoregressive Integrated Moving Average ◽  
Moving Average Model ◽  
Air Quality Monitoring Stations

In order to improve the accuracy of predicting the air pollutants in Shenzhen, a hybrid model based on ARIMA (Autoregressive Integrated Moving Average model) and prophet for mixing time and space relationships was proposed. First, ARIMA and Prophet method were applied to train the data from 11 air quality monitoring stations and gave them different weights. Then, finished the calculation about weight of impact in each air quality monitoring station to final results. Finally, built up the hybrid model and did the error evaluation. The result of the experiments illustrated that this hybrid method can improve the air pollutants prediction in Shenzhen.

scholarly journals VARMA-EGARCH Model for Air-Quality Analyses and Application in Southern Taiwan

Atmosphere ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 1096
Author(s):  
Edward Ming-Yang Wu ◽  
Shu-Lung Kuo
Keyword(s):  
Time Series ◽  
Air Quality ◽  
Air Pollutants ◽  
Quality Monitoring ◽  
Quality Data ◽  
Air Quality Monitoring ◽  
Egarch Model ◽  
Southern Taiwan ◽  
Air Quality Monitoring Stations ◽  
Monitoring Stations

This study adopted the Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model to analyze seven air pollutants (or the seven variables in this study) from ten air quality monitoring stations in the Kaohsiung–Pingtung Air Pollutant Control Area located in southern Taiwan. Before the verification analysis of the EGARCH model is conducted, the air quality data collected at the ten air quality monitoring stations in the Kaohsiung–Pingtung area are classified into three major factors using the factor analyses in multiple statistical analyses. The factors with the most significance are then selected as the targets for conducting investigations; they are termed “photochemical pollution factors”, or factors related to pollution caused by air pollutants, including particulate matter with particles below 10 microns (PM10), ozone (O3) and nitrogen dioxide (NO2). Then, we applied the Vector Autoregressive Moving Average-EGARCH (VARMA-EGARCH) model under the condition where the standardized residual existed in order to study the relationships among three air pollutants and how their concentration changed in the time series. By simulating the optimal model, namely VARMA (1,1)-EGARCH (1,1), we found that when O3 was the dependent variable, the concentration of O3 was not affected by the concentration of PM10 and NO2 in the same term. In terms of the impact response analysis on the predictive power of the three air pollutants in the time series, we found that the asymmetry effect of NO2 was the most significant, meaning that NO2 influenced the GARCH effect the least when the change of seasons caused the NO2 concentration to fluctuate; it also suggested that the concentration of NO2 produced in this area and the degree of change are lower than those of the other two air pollutants. This research is the first of its kind in the world to adopt a VARMA-EGARCH model to explore the interplay among various air pollutants and reactions triggered by it over time. The results of this study can be referenced by authorities for planning air quality total quantity control, applying and examining various air quality models, simulating the allowable increase in air quality limits, and evaluating the benefit of air quality improvement.

Emergent Method For Exposure Assessment To Air Pollutants Using Air Quality Monitoring Networks In Mexico

2015 ◽  
Vol 2015 (1) ◽  
pp. 3640
Author(s):  
José Luis Texcalac Sangrador ◽  
Karla Cervantes Martínez ◽  
Adrian Giovani Trejo González ◽  
Leonora Rojas Bracho ◽  
Horacio Riojas Rodríguez
Keyword(s):  
Air Quality ◽  
Exposure Assessment ◽  
Air Pollutants ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Monitoring Networks

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 Mapping pediatric A&E respiratory presentations with air quality

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Dermot Glackin
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
Air Quality ◽  
Air Pollutants ◽  
Causal Link ◽  
Quality Monitoring ◽  
Quality Data ◽  
Air Quality Monitoring ◽  
Respiratory Condition ◽  
Air Quality Data

IntroductionWest Belfast Partnership Board undertook a collaborative public health investigation to explore what if any correlation exists between air quality and children from the Falls Divis area presenting to RBHSC with a respiratory condition. BackgroundAn analysis of Emergency Department Attendances showed monthly trend in 2015/16 differed from previous years with a peak in November 2015 which was 27% higher than the number of attendances in November of 2014. The overall increase in 2015/16 across the 4 main paediatric categories was 10%. The increase for respiratory was 34% higher. West Belfast accounted for 32% (248n) of this spike. There exists a compelling case for linkage between air quality and a range of conditions which is socially patterned. Falls and Divis area appears in the top 3 areas of multiple deprivation. ApproachWe identified periods of elevated paediatric presentation at A&E with repository compliant and mapped over air quality monitoring data from the same period in the Falls Divis area; Factoring potential incubation time between exposure to potential harmful air pollutants. ConclusionBased upon a review of air quality data no causal link was established between air quality and periods of elevated presentation of children at A&E with respiratory evident.

scholarly journals NGHIÊN CỨU ỨNG DỤNG MẠNG TRẠNG THÁI PHẢN HỒI ĐỂ DỰ BÁO CHẤT LƯỢNG KHÔNG KHÍ

2016 ◽  
Vol 54 (1) ◽  
pp. 54 ◽  
Author(s):  
Mac Duy Hung ◽  
Nghiem Trung Dung
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Meteorological Parameters ◽  
Air Pollutant ◽  
Training Data ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Multilayer Perception ◽  
Testing Data ◽  
Better Than

A study on the application of Echo State Network (ESN) for the forecast of air quality in Hanoi for a period of seven days, which is based on the nonlinear relationships between the concentrations of an air pollutant to be forecasted and meteorological parameters, was conducted. Three air pollutants being SO2, NO2 and PM10 were selected for this study. Training data and testing data were extracted from the database of Lang air quality monitoring station, Hanoi, from 2003 to 2009. Values forecasted by ESN are compared with those by MLP (Multilayer Perception). Results shown that, in almost experiments, the performance of ESN is better than that of MLP in terms of the values and the correlation of concentration trends. The averages of RMSE of ESN and MLP for SO2 are 5.9 ppb and 6.9 ppb, respectively. For PM10, the accuracy of ESN is 83.8% with MAE of 53.5 μg/m3, while the accuracy of MLP is only 77.6% with MAE of 68.2 μg/m3. For NO2, the performance of ESN and MLP is similar; the accuracy of both models is in the range of 60% to 72.7%. These suggest that, ESN is a novel and feasible approach to build the air forecasting model. Keywords: Forecast, air quality, ESN, MLP, ANN, Hanoi, Vietnam.

scholarly journals Elucidating multipollutant exposure across a complex metropolitan area by systematic deployment of a mobile laboratory

2012 ◽  
Vol 12 (12) ◽  
pp. 31585-31627 ◽  
Author(s):  
I. Levy ◽  
C. Mihele ◽  
G. Lu ◽  
J. Narayan ◽  
N. Hilker ◽  
...  
Keyword(s):  
Air Quality ◽  
Urban Areas ◽  
Air Pollutants ◽  
Quality Monitoring ◽  
Spatiotemporal Variability ◽  
Small Scale ◽  
Emission Sources ◽  
Air Quality Monitoring ◽  
Mobile Laboratory ◽  
Mobile Measurements

Abstract. In urban areas, air quality is the outcome of multiple emission sources, each emitting a different combination of air pollutants. The result is a complex mixture of pollutants with a different spatiotemporal variability for each constituent. Studies exploring average spatial patterns across urban areas typically rely on air quality monitoring networks of a few sites, short multi-site saturation monitoring campaigns measuring a limited number of pollutants and/or air quality models. Each of these options has limitations. This study elucidates the main complexities of urban air quality with respect to small scale spatial differences for multiple pollutants so as to gain a better understanding of the variability in exposure estimates in urban areas. Mobile measurements of 23 air pollutants were taken at high resolution in Montreal, Quebec, Canada, and examined with respect to space, time and their interrelationships. The same route was systematically followed on 34 measurement days spread over different seasons and measurements were compared to adjacent air quality monitoring network stations. This approach allowed linkage of the mobile measurements to the network observations and to generate average maps that provide reliable information on the typical, annual average spatial pattern. Sharp differences in the spatial distribution were found to exist between different pollutants on the sub-urban scale, i.e. the neighbourhood to street scales, even for pollutants usually associated with the same specific sources. Nearby microenvironments may have a wide range in average pollution levels varying by up to 300%, which may cause large misclassification errors in estimating chronic exposures in epidemiological studies. For example, NO2 measurements next to a main road microenvironment are shown to be 210–265% higher than levels measured at a nearby urban background monitoring site, while black carbon is higher by 180–200% and ultrafine particles are 300% higher. For some pollutants (e.g. SO2 and benzene), there is good correspondence on a large scale due to similar emission sources, but differences on a small scale in proximity to these sources. Moreover. hotspots of different pollutants were identified and quantified. These results demonstrate the ability of an independent heavily instrumented mobile laboratory survey to quantify the representativeness of the monitoring sites to unmonitored locations, reveal the complex relationships between pollutants and understand chronic multi-pollutant exposure patterns associated with outdoor concentrations in an urban environment.

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.

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