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

2018 ◽  
Vol 15 (12) ◽  
pp. 2860 ◽  
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.

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

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.

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.

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

2021 ◽  
Vol 4 (3) ◽  
pp. 44
Author(s):  
Calorine Katushabe ◽  
Santhi Kumaran ◽  
Emmanuel Masabo
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Air Pollutant ◽  
Air Quality Index ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Pollutant Concentrations ◽  
Kampala City ◽  
Air Pollutant Concentrations

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.

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

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.

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

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.

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 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.

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