Relationship between PM10, NO2 and particle number concentration: validity of air quality controls

Aurangojeb, Mohammad

doi:10.1016/j.proenv.2011.05.007

Cited by 13 publications

(5 citation statements)

References 13 publications

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“…This study also found no relationship between the AECOPD and the levels of NO 2 or SO 2 . 13 The relationship between the AECOPD and the outdoor temperature was studied by Almagro et al who found that the decrease of temperature by 1 degree resulted in 4.7% increase in the mean number of hospitalizations. 14 It is worth noting that the negative correlation we found between the AECOPD and the outdoor temperature was stronger in days with PM 10 level above 50 μg/m 3 .…”

Section: Levels Of Pm 10mentioning

confidence: 99%

Impact of Air Pollution and Outdoor Temperature on the Rate of Chronic Obstructive Pulmonary Disease Exacerbations

et al. 2017

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Citation: Krachunov II, Kyuchukov NH, Ivanova ZI, Yanev NA, Hristova PA, Borisova ED, Popova TP, Pavlov PS, Nikolova PT, Ivanov YY. Impact of air pollution and outdoor temperature on the rate of chronic obstructive pulmonary disease exacerbations.Folia Medica 2017;59(4): 423-9. doi: 10.1515/folmed-2017-0053 Background: Environmental pollution can be one of the main risk factors for acute exacerbations of chronic obstructive pulmonary disease (COPD). Aim: To study the relationship between air pollution, outdoor temperature and exacerbations of COPD. Materials and methods: COPD patients (n=1432) were followed up for one year. The levels of particulate matter up to 10 μm (PM 10 ), nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ) and outside temperatures were collected from the Environmental Agency database.Results: A total of 309 acute COPD exacerbations (AECOPD) were recorded in the analysis. The daily mean concentrations of PM 10 were found to correlate signifi cantly with the daily mean concentrations of NO 2 and SO 2 (ρ 0.34 and ρ 0.49, respectively; p=0.0001). The negative correlations between the daily mean temperature and the daily mean levels of PM 10 , NO 2 and SO 2 were also signifi cant (ρ -0.44, ρ -0.11, and ρ -0.37, respectively; p=0.0001). The daily number of AECOPD correlated with the mean levels of PM 10 in the previous six days (ρ 0.14; p=0.02) and the lower outdoor temperature (ρ -0.2; p=0.001). The negative correlation between the daily number of AECOPD and the mean daily temperature was stronger in days with levels of PM 10 above 50 μg/m 3 (ρ -0.3 p=0.02 vs. ρ -0.18 p= 0.01). Conclusion: Lower daily mean temperatures were associated with the levels of air pollutants. The level of PM 10 correlated with the levels of the other air pollutants. The daily number of AECOPD was found to correlate weakly, but signifi cantly with the mean level of PM 10 in the previous six days. BACKGROUND

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Section: Levels Of Pm 10mentioning

confidence: 99%

Impact of Air Pollution and Outdoor Temperature on the Rate of Chronic Obstructive Pulmonary Disease Exacerbations

et al. 2017

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“…Another challenge facing air quality forecasting is the large number of factors that influence air pollutants' concentrations. Two of the most known factors to impact air quality are meteorological and road traffic-related features [16,17]. In [18,19], Banarjee et al studied the impact of meteorological features, including temperature, humidity, and wind on the concentration of pollutants in India.…”

Section: Introductionmentioning

confidence: 99%

MoreAir: A Low-Cost Urban Air Pollution Monitoring System

Gryech

Ben-Aboud

Guermah

et al. 2020

Sensors

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MoreAir is a low-cost and agile urban air pollution monitoring system. This paper describes the methodology used in the development of this system along with some preliminary data analysis results. A key feature of MoreAir is its innovative sensor deployment strategy which is based on mobile and nomadic sensors as well as on medical data collected at a children’s hospital, used to identify urban areas of high prevalence of respiratory diseases. Another key feature is the use of machine learning to perform prediction. In this paper, Moroccan cities are taken as case studies. Using the agile deployment strategy of MoreAir, it is shown that in many Moroccan neighborhoods, road traffic has a smaller impact on the concentrations of particulate matters (PM) than other sources, such as public baths, public ovens, open-air street food vendors and thrift shops. A geographical information system has been developed to provide real-time information to the citizens about the air quality in different neighborhoods and thus raise awareness about urban pollution.

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“…On the other hand, the variations of mass concentrations of PM 2.5 , PM 10 , SO 2 , NO 2 , CO, and O 3 were analyzed based on data from 286 monitoring sites obtained for one year. By comparing the pollutant concentrations over this length of time, the characteristics of variations of the mass concentrations of air pollutants were determined using the Pearson correlation coefficient, which establishes that a relationship between PM 2.5 , PM 10 , and the gas pollutants exists [46][47][48]. A lot of studies have provided transferable models to estimate the health effects of air pollutants to support the creation of environmental health public policies for national and international intervention [49].…”

Section: Gas Considerationsmentioning

confidence: 99%

Smart Multi-Sensor System for Remote Air Quality Monitoring Using Unmanned Aerial Vehicle and LoRaWAN

Camarillo-Escobedo

Flores

Marin-Montoya

et al. 2022

Sensors

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Deaths caused by respiratory and cardiovascular diseases have increased by 10%. Every year, exposure to high levels of air pollution is the cause of 7 million premature deaths and the loss of healthy years of life. Air pollution is generally caused by the presence of CO, NO2, NH3, SO2, particulate matter PM10 and PM2.5, mainly emitted by economic activities in large metropolitan areas. The problem increases considerably in the absence of national regulations and the design, installation, and maintenance of an expensive air quality monitoring network. A smart multi-sensor system to monitor air quality is proposed in this work. The system uses an unmanned aerial vehicle and LoRa communication as an alternative for remote and in-situ atmospheric measurements. The instrumentation was integrated modularly as a node sensor to measure the concentration of carbon monoxide (CO), nitrogen dioxide (NO2), ammonia (NH3), sulfur dioxide (SO2), and suspended particulate mass PM10 and PM2.5. The optimal design of the multi-sensor system has been developed under the following constraints: A low weight, compact design, and low power consumption. The integration of the multi-sensor device, UAV, and LoRa communications as a single system adds aeeded flexibility to currently fixed monitoring stations.

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Relationship between PM10, NO2 and particle number concentration: validity of air quality controls

Cited by 13 publications

References 13 publications

Impact of Air Pollution and Outdoor Temperature on the Rate of Chronic Obstructive Pulmonary Disease Exacerbations

Impact of Air Pollution and Outdoor Temperature on the Rate of Chronic Obstructive Pulmonary Disease Exacerbations

MoreAir: A Low-Cost Urban Air Pollution Monitoring System

Smart Multi-Sensor System for Remote Air Quality Monitoring Using Unmanned Aerial Vehicle and LoRaWAN

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