Air Monitoring Stations Preliminary Real Estate Plan

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.

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

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b2-553-2016 ◽

2016 ◽

Vol XLI-B2 ◽

pp. 553-554 ◽

Cited By ~ 2

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. 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.

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Statistical Forecast Applied to Two Macao Air Monitoring Stations

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/489/1/012018 ◽

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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Magnetic monitoring of PM filters from Air Monitoring Stations during the COVID-19 lockdown in Rome, Italy (March 10th - May 18th, 2020)

10.5194/egusphere-egu21-3817 ◽

2021 ◽

Author(s):

Aldo Winkler ◽

Antonio Amoroso ◽

Alessandro Di Giosa ◽

Giada Marchegiani

Keyword(s):

Magnetic Susceptibility ◽

Air Quality ◽

Urban Areas ◽

Magnetic Measurements ◽

Air Monitoring ◽

Magnetic Minerals ◽

Concentration Data ◽

Local Conditions ◽

The Impact ◽

Monitoring Stations

An extensive survey of the magnetic properties of PM filters from selected air monitoring stations in Rome and other localities in Latium Region (Sacco Valley, Civitavecchia, Fiumicino) was conducted for outlining the impact of the lockdown measures on air quality.The magnetic measurements highlighted a relevant content of magnetic minerals, mostly attributable to traffic related sources, on the filters from two stations in Rome and two stations from the urban areas of Civitavecchia and Fiumicino.The PM filters from the Sacco Valley showed reduced concentrations of magnetic minerals, compared to Rome, however higher than the Castel Di Guido and Civitavecchia Sant'Agostino control stations.The daily PM concentration data did not generally correlate with the mass susceptibility data, indicating that PM was often dominated by non-ferromagnetic contents, presumably due to wind-driven natural dusts, as stressed by the frequent anticorrelation between mass magnetic susceptibility and PM concentration.In Magnagrecia air quality station, Rome, the average values &#8203;&#8203;of the concentration depending magnetic parameters resulted about a half of those measured in 2005 on the filters from the same station.From the Day plot, the filters with higher magnetic susceptibility values showed relatively coarse magnetite-like particles as the main magnetic minerals, ascribable to non-exhaust PM emissions from brakes.This study confirmed that the interpretation of PM concentration during the lockdown is not straightforward and depends on many factors, such as natural inputs, resuspension and local conditions; anyway, magnetic analyses confirmed to be a valuable tool in PM source apportionment and concentration data interpretation.

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