Underestimated or overestimated? Dynamic assessment of hourly PM2.5 exposure in the metropolitan area based on heatmap and micro-air monitoring stations

2021 ◽  
pp. 146283
Author(s):  
Xin Li ◽  
Tao Yang ◽  
Zhuotong Zeng ◽  
Xiaodong Li ◽  
Guangming Zeng ◽  
...  
Keyword(s):  
Metropolitan Area ◽  
Dynamic Assessment ◽  
Air Monitoring ◽  
Pm2.5 Exposure ◽  
Monitoring Stations

scholarly journals Temporal Air Quality (NO2, O3 and PM10) Changes in Urban and Rural Stations in Catalonia during COVID-19 Lockdown: An Association with Human Mobility and Satellite Data

2021 ◽  
Author(s):  
Eva Gorrochategui ◽  
Isabel Hernandez ◽  
Eva Pérez-Gabucio ◽  
Sílvia Lacorte ◽  
Romà Tauler
Keyword(s):  
Air Quality ◽  
Metropolitan Area ◽  
Satellite Data ◽  
Satellite Images ◽  
Human Mobility ◽  
Control Site ◽  
Air Monitoring ◽  
Mobility Index ◽  
Ski Resort ◽  
Monitoring Stations

Abstract In this study, changes in air quality by NO2, O3 and PM10 in Barcelona metropolitan area and other parts of Catalonia during the COVID-19 lockdown with respect to pre-lockdown and to previous years (2018 and 2019) were evaluated. Selected air monitoring stations included 3 urban (Gràcia, Vall d’Hebron and Granollers), 1 control site (Fabra Observatory), 1 semi-urban (Manlleu), and 3 rural (Begur, Bellver de Cerdanya, and Juneda). NO2 lockdown levels showed a diminution, which in relative terms was maximum in two rural stations (Bellver de Cerdanya, -63% and Begur, -61%), presumably due to lower emissions from the ceasing hotel and ski resort activities during eastern holidays. In absolute terms and from an epidemiologic perspective, decrease in NO2, also reinforced by the high amount of rainfall registered in April 2020, was more relevant in the urban stations around Barcelona. O3 levels increased in the transited urban stations (Gràcia, +42%, and Granollers, +64%) due to the lower titration effect by NOx. PM10 lockdown levels decreased, mostly in Gràcia, Vall d’Hebron and Granollers (-35, -39% and -39%, respectively) due to traffic depletion (-90% in Barcelona's transport). Correlation among mobility index in Barcelona (-100% in retail & recreation) and contamination was positive for NO2 and PM10 and negative for O3 (P<0.001). Satellite images evidenced two hotspots of NO2 in Spain (Madrid and Barcelona) in April 2018 and 2019 that disappeared in 2020. Overall, the benefits of lockdown on air quality in Catalonia were evidenced with NO2, O3 and PM10 levels below WHOAQG values in most of stations opposed to the excess registered in previous years.

scholarly journals Temporal Air Quality (NO2, O3 and PM10) Changes in Urban and Rural Stations in Catalonia During COVID-19 Lockdown: An Association with Human Mobility and Satellite Data

2021 ◽  
Author(s):  
Eva Gorrochategui ◽  
Isabel Hernandez ◽  
Eva Pérez-Gabucio ◽  
Sílvia Lacorte ◽  
Romà Tauler
Keyword(s):  
Air Quality ◽  
Metropolitan Area ◽  
Satellite Data ◽  
Satellite Images ◽  
Human Mobility ◽  
Control Site ◽  
Air Monitoring ◽  
Mobility Index ◽  
Ski Resort ◽  
Monitoring Stations

Abstract In this study, changes in air quality by NO2, O3 and PM10 in Barcelona metropolitan area and other parts of Catalonia during the COVID-19 lockdown with respect to pre-lockdown and to previous years (2018 and 2019) were evaluated. Selected air monitoring stations included 3 urban (Gràcia, Vall d’Hebron and Granollers), 1 control site (Observatori Fabra), 1 semi-urban (Manlleu), and 3 rural (Begur, Bellver de Cerdanya, and Juneda). NO2 lockdown levels showed a diminution, which in relative terms was maximum in two rural stations (Bellver de Cerdanya, -63% and Begur, -61%), presumably due to lower emissions from the ceasing hotel and ski resort activities during eastern holidays. In absolute terms and from an epidemiologic perspective, decrease in NO2 was more relevant in the urban stations around Barcelona. O3 levels increased in the transited urban stations (Gràcia, +42%, and Granollers, +64%) due to the lower titration effect by NOx. PM10 lockdown levels decreased, mostly in Gràcia, Vall d’Hebron and Granollers (-35, -39% and -39%, respectively) due to traffic depletion (-90% in Barcelona's transport). Correlation among mobility index in Barcelona (-100% in retail & recreation) and contamination was positive for NO2 and PM10 and negative for O3 (P<0.001). Satellite images evidenced two hotspots of NO2 in Spain (Madrid and Barcelona) in April 2018 and 2019 that disappeared in 2020. Overall, the benefits of lockdown on air quality in Catalonia were evidenced with NO2, O3 and PM10 levels below WHOAQG values in most of stations opposed to the excess registered in previous years.

Association of PM2.5 Exposure and Health Outcomes in a Representative Population Sample of the Mexico City Metropolitan Area Using Satellite and Monitor-Based Exposure Estimations

2018 ◽  
Vol 2018 (1) ◽  
Author(s):  
Martha María Téllez-Rojo ◽  
Stephen J. Rothenberg ◽  
José Luis Texcalac-Sangrador ◽  
Allan Just ◽  
Itai Kloog ◽  
...  
Keyword(s):  
Health Outcomes ◽  
Mexico City ◽  
Metropolitan Area ◽  
Population Sample ◽  
Pm2.5 Exposure

scholarly journals Measurements of PM<sub>2.5</sub> with PurpleAir under atmospheric conditions

2019 ◽  
Author(s):  
Karin Ardon-Dryer ◽  
Yuval Dryer ◽  
Jake N. Williams ◽  
Nastaran Moghimi
Keyword(s):  
Monitoring System ◽  
Environmental Protection Agency ◽  
Low Cost ◽  
Air Monitoring ◽  
Atmospheric Conditions ◽  
Distributed Monitoring ◽  
Promising Tool ◽  
Concentration Changes ◽  
Air Quality Monitoring Stations ◽  
Monitoring Stations

Abstract. The PurpleAir PA-II unit is a low-cost sensor for monitoring changes in the concentrations of Particulate Matter (PM) of various sizes. There are currently more than 9000 PA-II units worldwide; some of them are located in areas where no other reference air monitoring system is present. Previous studies have examined the performance of these PA-II units (or the sensor within them) in comparison to a co-located reference air monitoring system. However, because PA-II units are installed by PurpleAir customers, the PA-II units are not co-located with a reference air monitoring system and, in many cases, are not near one. This study aimed to examine how PA-II units perform under atmospheric conditions when exposed to a variety of pollutants and PM2.5 concentrations. We were interested in knowing how accurate these PA-II units are when measuring PM2.5 concentrations with their sensitivity to concentration changes in comparison to the Environmental Protection Agency (EPA) Air Quality Monitoring Stations (AQMS) that are not co-located with them. For this study, we selected eight different locations, where each location contains multiple PA-II units (minimum of seven per location, a total of 86 units) and at least one AQMS (total of 14). PM2.5 measurements from each PA-II unit were compared to those from the AQMS and other PA-II units in its area. The comparisons were made based on hourly and daily PM2.5 measurements. In most cases, the AQMS and PA-II units were found to be in good agreement; they measured similar values and followed similar trends, that is, when the PM2.5 values measured by the AQMS increased or decreased, so did those of the PA-II. In some high-pollution events, the PA-II measured higher PM2.5 values compared to those measured by the AQMS. We found PA-II PM2.5 measurements to remain unaffected by changes in temperature or Relative Humidity (RH). Overall, the PA-II unit seems to be a promising tool for identifying relative changes in PM2.5 concentration with the potential to complement sparsely distributed monitoring stations and to aid in assessing and minimizing the public exposure to PM, particularly in areas lacking the presence of an AQMS.

scholarly journals Using geo-targeted social media data to detect outdoor air pollution

2016 ◽  
Vol XLI-B2 ◽  
pp. 553-554 ◽  
Author(s):  
W. Jiang ◽  
Y. Wang ◽  
M. H. Tsou ◽  
X. Fu
Keyword(s):  
Social Media ◽  
Air Pollution ◽  
Air Quality ◽  
Environmental Changes ◽  
Real Space ◽  
Air Monitoring ◽  
Outdoor Air ◽  
Outdoor Air Pollution ◽  
Media Messages ◽  
Monitoring Stations

Outdoor air pollution has become a more and more serious issue over recent years (He, 2014). Urban air quality is measured at air monitoring stations. Building air monitoring stations requires land, incurs costs and entails skilled technicians to maintain a station. Many countries do not have any monitoring stations and even lack any means to monitor air quality. Recent years, the social media could be used to monitor air quality dynamically (Wang, 2015; Mei, 2014). However, no studies have investigated the inter-correlations between real-space and cyberspace by examining variation in micro-blogging behaviors relative to changes in daily air quality. Thus, existing methods of monitoring AQI using micro-blogging data shows a high degree of error between real AQI and air quality as inferred from social media messages. &lt;br&gt;&lt;br&gt; In this paper, we introduce a new geo-targeted social media analytic method to (1) investigate the dynamic relationship between air pollution-related posts on Sina Weibo and daily AQI values; (2) apply Gradient Tree Boosting, a machine learning method, to monitor the dynamics of AQI using filtered social media messages. Our results expose the spatiotemporal relationships between social media messages and real-world environmental changes as well suggesting new ways to monitor air pollution using social media.

scholarly journals Statistical Forecast Applied to Two Macao Air Monitoring Stations

2020 ◽  
Vol 489 ◽  
pp. 012018
Author(s):  
M Lei ◽  
J Monjardino ◽  
L Mendes ◽  
D Gonçalves ◽  
F Ferreira
Keyword(s):  
Air Monitoring ◽  
Monitoring Stations
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