Fuzzy Based Prediction Model for Air Quality Monitoring for Kampala City in East Africa

The quality of air affects lives and the environment at large. Poor air quality has claimed many lives and distorted the environment across the globe, and much more severely in African countries where air quality monitoring systems are scarce or even do not exist. Here in Africa, dirty air is brought about by the growth in industrialization, urbanization, flights, and road traffic. Air pollution remains such a silent killer, especially in Africa, and if not dealt with, it will continue to lead to health issues, such as heart conditions, stroke, and chronic respiratory organ unwellness, which later result in death. In this paper, the Kampala Air Quality Index prediction model based on the fuzzy logic inference system was designed to determine the air quality for Kampala city, according to the air pollutant concentrations (nitrogen dioxide, sulphur dioxide and fine particulate matter 2.5). It is observed that fuzzy logic algorithms are capable of determining the air quality index and therefore, can be used to predict and estimate the air quality index in real time, based on the given air pollutant concentrations. Hence, this can reduce the effects of air pollution on both humans and the environment.

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La surveillance de la qualité de l'air dans les Hauts-de-France

La Météorologie ◽

10.37053/lameteorologie-2021-0092 ◽

2021 ◽

pp. 045

Author(s):

Jimmy Leyes ◽

Laure Roussel

Keyword(s):

Air Quality ◽

Air Pollutant ◽

Quality Monitoring ◽

Air Quality Monitoring ◽

Non Profit ◽

The Public ◽

Pollutant Concentrations ◽

Ambient Concentrations ◽

Air Pollutant Concentrations ◽

Qualité De L’Air

La surveillance réglementaire de la qualité de l'air en France est confiée aux associations régionales agréées de surveillance de la qualité de l'air (Aasqa) telles qu'Atmo Hauts-de-France. Elles s'appuient sur une palette d'outils et leur expertise pour mesurer les polluants dans l'air de leur territoire, alerter les populations en cas d'épisode de pollution, répondre aux exigences réglementaires de surveillance définies au niveau européen, tout en prenant en compte les spécificités régionales. Cet article présente les différents outils utilisés par les Aasqa, et plus particulièrement Atmo Hauts-de-France, pour surveiller et estimer la qualité de l'air. L'association régionale opère ainsi un ensemble de stations de mesures fixes et mobiles pour suivre en continu les concentrations de polluants réglementés ou non sur son territoire, et dispose d'outils de modélisation pour évaluer et prévoir la qualité de l'air en tous points de la région. Cet article présente également certains des paramètres météorologiques qui influencent la qualité de l'air de la région Hauts-de-France, particulièrement concernée par les épisodes de pollution aux particules. Regulatory air quality monitoring in France is performed by government-approved non-profit organisations called AASQAs, one of which is Atmo Hauts-de-France. These organisations rely on decades of accumulated air quality expertise and use several techniques to measure air pollutant concentrations, inform the public when pollutant levels are unhealthy, and comply with E.U. air quality monitoring regulations. This paper gives an overview of the tools used by AASQAs, and more particularly by Atmo Hauts-de-France, to monitor and forecast air quality. The year-round continuous monitoring of air pollutant levels at fixed sites is supplemented by short-term measurements made with fully-equipped vehicles or trailers and by modelling tools that forecast air quality and estimate pollutant levels where there are no measurements. AASQAs study pollutants which ambient concentrations are regulated by European air quality standards as well as other pollutants which are not regulated in this way. This work also discusses some of the meteorological factors, that affect air quality in the region Hauts-de-France, which is heavily impacted by particulate matter pollution.

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The Assessment of Air Pollutant Concentrations and Air Quality Index in Shiraz during 2011-2013

Journal of Environmental Health Engineering ◽

10.18869/acadpub.jehe.3.3.182 ◽

2016 ◽

Vol 3 (3) ◽

pp. 182-192 ◽

Cited By ~ 1

Author(s):

Monireh Majlesi Nasr ◽

Mohammad Ansarizadeh ◽

Mostafa Leili ◽

◽

...

Keyword(s):

Air Quality ◽

Quality Index ◽

Air Pollutant ◽

Air Quality Index ◽

Pollutant Concentrations ◽

Air Pollutant Concentrations

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A short review on different techniques used for site selection of air quality monitoring stations

Journal of Industrial Engineering and Decision Making ◽

10.31181/jiedm200201026h ◽

2021 ◽

Vol 2 (1) ◽

pp. 27-30

Author(s):

Hemanth Kollati ◽

◽

Animesh Debnath ◽

Keyword(s):

Air Pollution ◽

Air Quality ◽

Site Selection ◽

Quality Index ◽

Air Quality Index ◽

Quality Monitoring ◽

Air Quality Monitoring ◽

Air Quality Monitoring Stations ◽

Selection Of ◽

Monitoring Stations

Air pollution has become a serious concern across the world in the last few decades. In specific cities, the air quality index value had changed from very unhealthy to a hazardous level of health concern. Air pollution has a serious impact on daily lives in those cities. Monitoring of air pollution is becoming necessary these days. Air quality monitoring stations are installed to get the air pollution data, which indicates in the air quality index (AQI) value. In order to contain a proper air quality index (AQI) value, it is essential to locate the air quality monitoring stations in the appropriate place of the study area. Several techniques were being used for site selection of air quality monitoring stations for the last few decades. In this short review, all such techniques have been studied systematically, and comprehensive analysis has been reported for further use by the scientific community and policymakers. In this study, the methods used in the site selection of air quality monitoring stations were categorized into four groups. (1) Multi-Criteria Decision Making (MCDM) techniques; (2) Geographical Information System (GIS); (3) hybrid techniques; and (4) miscellaneous. In the site selection of air quality monitoring stations, the decision-makers should consider various parameters based on the study area. While considering various parameters, the problem solving becomes complex. At this point, MCDM techniques, GIS, and Hybrid techniques are found to be helpful tools for the decision-makers.

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Air Quality Index and Air Pollutant Concentration Prediction Based on Machine Learning Algorithms

Applied Sciences ◽

10.3390/app9194069 ◽

2019 ◽

Vol 9 (19) ◽

pp. 4069 ◽

Cited By ~ 2

Author(s):

Huixiang Liu ◽

Qing Li ◽

Dongbing Yu ◽

Yu Gu

Keyword(s):

Machine Learning ◽

Air Pollution ◽

Air Quality ◽

Regression Models ◽

Quality Index ◽

Air Pollutant ◽

Air Quality Index ◽

Machine Learning Algorithms ◽

Coefficient Of Determination ◽

Support Vector

Air pollution has become an important environmental issue in recent decades. Forecasts of air quality play an important role in warning people about and controlling air pollution. We used support vector regression (SVR) and random forest regression (RFR) to build regression models for predicting the Air Quality Index (AQI) in Beijing and the nitrogen oxides (NOX) concentration in an Italian city, based on two publicly available datasets. The root-mean-square error (RMSE), correlation coefficient (r), and coefficient of determination (R2) were used to evaluate the performance of the regression models. Experimental results showed that the SVR-based model performed better in the prediction of the AQI (RMSE = 7.666, R2 = 0.9776, and r = 0.9887), and the RFR-based model performed better in the prediction of the NOX concentration (RMSE = 83.6716, R2 = 0.8401, and r = 0.9180). This work also illustrates that combining machine learning with air quality prediction is an efficient and convenient way to solve some related environment problems.

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Association of Exposure to Fine-Particulate Air Pollution and Acidic Gases with Incidence of Nephrotic Syndrome

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph15122860 ◽

2018 ◽

Vol 15 (12) ◽

pp. 2860 ◽

Cited By ~ 6

Author(s):

Shih-Yi Lin ◽

Wu-Huei Hsu ◽

Cheng-Li Lin ◽

Cheng-Chieh Lin ◽

Chih-Hsueh Lin ◽

...

Keyword(s):

Air Pollution ◽

Nephrotic Syndrome ◽

Air Quality ◽

Air Pollutant ◽

Quality Monitoring ◽

Air Quality Monitoring ◽

Residential Areas ◽

Acidic Gases ◽

Increased Risk ◽

Four Levels

Background: Air pollution has been associated with autoimmune diseases. Nephrotic syndrome is a clinical manifestation of immune-mediated glomerulopathy. However, the association between nephrotic syndrome and air pollution constituents remains unknown. We conducted this nationwide retrospective study to investigate the association between PM2.5 and nephrotic syndrome. Methods: We used the Longitudinal Health Insurance Database (LHID) and the Taiwan Air Quality-Monitoring Database (TAQMD). We combined and stratified the LHID and the TAQMD data by residential areas of insurants linked to nearby air quality-monitoring stations. Air pollutant concentrations were grouped into four levels based on quartile. Univariable and multivariable Cox proportional hazard regression models were applied. Findings: Relative to Q1-level SO2, subjects exposed to the Q4 level were associated with a 2.00-fold higher risk of nephrotic syndrome (adjusted HR = 2.00, 95% CI = 1.66–2.41). In NOx, relative to Q1 NOx concentrations, the adjusted HRs of nephrotic syndrome risk were 1.53 (95% CI = 1.23–1.91), 1.30 (95% CI = 1.03–1.65), and 2.08 (95% CI = 1.69–2.56) for Q2, Q3, and Q4 levels, respectively. The results revealed an increasing trend for nephrotic syndrome risk correlating with increasing levels of NO, NO2, and PM2.5 concentrations. Interpretation: High concentrations of PM2.5, NO, NO2, and SO2 are associated with increased risk of nephrotic syndrome.

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30 Years of Air Quality Trends in Japan

Atmosphere ◽

10.3390/atmos12081072 ◽

2021 ◽

Vol 12 (8) ◽

pp. 1072

Author(s):

Akiyoshi Ito ◽

Shinji Wakamatsu ◽

Tazuko Morikawa ◽

Shinji Kobayashi

Keyword(s):

Air Pollution ◽

Air Quality ◽

Air Pollutant ◽

Control Measures ◽

Related Substances ◽

Pollutant Concentrations ◽

Exhaust Particles ◽

Air Pollutant Concentrations ◽

Atmospheric Concentrations ◽

Pollution Concentrations

The aim of this paper is to obtain information that will contribute to measures and research needed to further improve the air quality in Japan. The trends and characteristics of air pollutant concentrations, especially PM2.5, ozone, and related substances, over the past 30 years, are analyzed, and the relationships between concentrations and emissions are discussed quantitatively. We found that PM2.5 mass concentrations have decreased, with the largest reduction in elemental carbon (EC) as the PM2.5 component. The concentrations of organic carbon (OC) have not changed significantly compared to other components, suggesting that especially VOC emissions as precursors need to be reduced. In addition, the analysis of the differences in PM2.5 concentrations between the ambient and the roadside showed that further research on non-exhaust particles is needed. For NOx and SO2, there is a linear relationship between domestic anthropogenic emissions and atmospheric concentrations, indicating that emission control measures are directly effective in the reduction in concentrations. Also, recent air pollution episodes and the effect of reduced economic activity, as a consequence of COVID-19, on air pollution concentrations are summarized.

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Evaluation of Redundant Measurements on the Air Quality Monitoring Network of Lisbon and Tagus Valley

Chemical Product and Process Modeling ◽

10.2202/1934-2659.1280 ◽

2009 ◽

Vol 4 (4) ◽

Author(s):

José C. M. Pires ◽

Fernando G. Martins ◽

Maria C. M. Alvim-Ferraz ◽

Maria C. Pereira

Keyword(s):

Air Pollution ◽

Air Quality ◽

Monitoring Network ◽

Principal Component ◽

Original Data ◽

Air Pollutant ◽

Quality Monitoring ◽

Air Quality Monitoring ◽

Minimum Number ◽

Tagus Valley

The aim of this study was to evaluate redundant measurements in the air quality monitoring network (AQMN) of Lisbon and Tagus Valley (LTV). With this purpose, the minimum number of monitoring sites that should operate was achieved using principal component analysis (PCA). The air pollution data was collected in twenty monitoring sites during the period from January to December 2006. The air pollutants analysed were CO, NO2, PM10 and O3.In this study, a different criterion for selection of the number of principal components (PCs) was applied. The PCs were selected representing at least 95% of the original data variance. Using this criterion, the PCs have more information about the air pollution data, increasing the confidence in the PCA results.The PCA results showed that, from twenty studied monitoring sites, only ten for CO, eleven for NO2, five for O3 and nine for PM10 were needed to characterize the region. The air pollutant analysers corresponding to the redundant measurements can be installed in non-monitored regions, allowing the enlargement of the air quality monitoring network.

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Changes in Air Quality of the Yuxi City Urban Area

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1021.225 ◽

2014 ◽

Vol 1021 ◽

pp. 225-228

Author(s):

Cheng Qiu ◽

Hong Chen ◽

Chun Li Ye ◽

Yan Jun Yang ◽

Chang Bing Ye

Keyword(s):

Air Pollution ◽

Air Quality ◽

Urban Area ◽

Health Problem ◽

Quality Index ◽

Quality Standards ◽

Air Pollutant ◽

Air Quality Index ◽

Air Quality Standards

Air pollution causes health problem. The paper simply analyzed the changes of air quality in the Yuxi city urban area from 2006 to 2012. In the Yuxi city urban area between 2006 and 2012, SO2 levels increased about 43.9 percent; NO2 levels increased about 13.3 percent; PM10 levels in 2012 decreased about 1.5 percent. By evaluating the air quality in the Yuxi city urban area, the results showed that air quality index was the maximum in 2009, and the quality of the air in Yuxi became worse from 2006 to 2012, air pollution in 2009 was the heaviest between 2006 to 2012. After adopting P.R.C EPA air quality standards (GB3095-2012) in 2013, the first air pollutant in Yuxi is PM10, and then it is SO2 among SO2, NO2 and PM10.Much should beend done to reduce the amount of PM10 and SO2 released.

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Association Between Air Pollution and the Risk of Uveitis: A Nationwide, Population-Based Cohort Study

Frontiers in Immunology ◽

10.3389/fimmu.2021.613893 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yi-Chiao Bai ◽

Cheng-You Wang ◽

Cheng-Li Lin ◽

Jung-Nien Lai ◽

James Cheng-Chung Wei

Keyword(s):

Air Pollution ◽

Cohort Study ◽

Air Quality ◽

International Classification Of Diseases ◽

Population Based ◽

Air Pollutant ◽

Quality Monitoring ◽

Air Quality Monitoring ◽

Potential Risk Factors ◽

Four Levels

Previous studies have revealed an association between ocular surface disorders and air pollution, few studies have focused on the risk of uveitis. We aimed to investigate whether air pollution increases the risk of uveitis. We used the Taiwan Longitudinal Health Insurance Database (LHID) and the Taiwan Air Quality Monitoring Database (TAQMD) to conduct a retrospective cohort study. Air pollutant concentrations, including those of carbon dioxide (CO2), were grouped into four levels according to quartiles. The outcome was the incidence of uveitis, as defined in the International Classification of Diseases, Ninth Revision. We used univariable and multivariable Cox proportional hazard regression models to calculate the adjusted hazard ratios (aHRs) and determine the potential risk factors of uveitis. Overall, 175,489 subjects were linked to their nearby air quality monitoring stations. We found that for carbon monoxide, the aHRs of uveitis risk for the Q3 and Q4 levels were 1.41 (95% confidence interval (CI) = 1.23–1.61) and 2.19 (95% CI = 1.93–2.47), respectively, in comparison with those for the Q1 level. For nitric oxide, the aHRs for the Q3 and Q4 levels were 1.46 (95% CI = 1.27–1.67) and 2.05 (95% CI = 1.81–2.32), respectively. For nitrogen oxide (NOx), the aHRs for the Q2, Q3, and Q4 levels were 1.27 (95% CI = 1.11–1.44), 1.34 (95% CI = 1.16–1.53), and 1.85 (95% CI = 1.63–2.09), respectively. For total hydrocarbon (THC), the aHRs for the Q2, Q3, and Q4 levels were 1.42 (95% CI = 1.15–1.75), 3.80 (95% CI = 3.16–4.57), and 5.02 (95% CI = 4.19–6.02), respectively. For methane (CH4), the aHRs for the Q3 and Q4 levels were 1.94 (95% CI = 1.60–2.34) and 7.14 (95% CI = 6.01–8.48), respectively. In conclusion, air pollution was significantly associated with incidental uveitis, especially at high THC and CH4 levels. Furthermore, the uveitis risk appeared to increase with increasing NOx and THC levels.

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