Representativeness of an air quality monitoring station for PM2.5 and source apportionment over a small urban domain

Yatkin, Sinan; Gerboles, Michel; Belis, Claudio A.; Karagulian, Federico; Lagler, Friedrich; Maurizio, Barbiere; Annette, Borowiak

doi:10.1016/j.apr.2019.10.004

Cited by 28 publications

(11 citation statements)

References 27 publications

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“…A value of 0 for the COD implies that two sites are exactly alike, and a value larger than 0 implies that the two sites are dissimilar. Criteria of COD > 0.2 indicating heterogeneity and <0.1 indicating homogeneity were selected for the spatial variability analysis of PM 2.5 [ 10 , 35 ].…”

Section: Methodsmentioning

confidence: 99%

Chemical Composition and Source Apportionment of Wintertime Airborne PM2.5 in Changchun, Northeastern China

Zhang

Tong

et al. 2021

IJERPH

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This study presents field observations and laboratory analyses of wintertime airborne particulate matter (PM2.5) and its chemical components in the Changchun metropolitan area, the geographical center of northeastern China. Twenty-four hour PM2.5 filter samples were collected from 23 December 2011 to 31 January 2012 at four sites in the types of traffic, residential, campus, and a near-city rural village, respectively. Daily PM2.5 concentrations ranged from 49 to 466 µg m−3, with an arithmetic average of 143 µg m−3. Laboratory analyses showed that among all measured chemical species, mineral dust contributed the largest proportion (20.7%) to the total PM2.5 mass, followed by secondary inorganic aerosols (SIA, including SO42−, NO3− and NH4+), which constituted 18.8% of PM2.5 mass. Another notable feature of PM2.5 chemical composition was high halogen (Cl− and F−) loadings at all sites, which was likely due to emissions from coal combustion, plastic manufacturing, and glass melting. Among the four sampling sites, the suburban site exhibited the highest PM2.5 levels and extremely high Cl− and F− loadings due to residential wood burning and nearby industrial facilities lacking effective emission controls. Our results report one of the earliest observations of PM2.5 composition in this region, providing a baseline of aerosol profiles of aerosol before PM2.5 was routinely measured by environmental protection agencies in China, which could be useful for assessing long-term trends of air quality and effectiveness of mitigation measures.

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Section: Methodsmentioning

confidence: 99%

Chemical Composition and Source Apportionment of Wintertime Airborne PM2.5 in Changchun, Northeastern China

Zhang

Tong

et al. 2021

IJERPH

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“…Stationary monitoring sensors are known to represent concentrations of their immediate surroundings. 7 However, recently developed models (e.g., Stochastic Human Exposure and Dose Simulation, SHEDS; American Meteorological Society/Environmental Protection Agency Regulatory Model, AERMOD; Research-LINE, RLINE) incorporate other variables, including pollution emissions data, topography, meteorological data, satellite data, and micro-environmental characteristics, and offer higher resolution in ambient concentrations. 6 8 Nowadays, efforts are being introduced to further enhance the accuracy and resolution of estimated concentrations with new data sources discussed below.…”

Section: Section 1: Air Pollution and Digital Health Technologiesmentioning

confidence: 99%

Digital Healthcare for Airway Diseases from Personal Environmental Exposure

2022

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Digital technologies have emerged in various dimensions of human life, ranging from education to professional services to well-being. In particular, health products and services have expanded by the use and development of artificial intelligence, mobile health applications, and wearable electronic devices. Such advancements have enabled accurate and updated tracking and modeling of health conditions. For instance, digital health technologies are capable of measuring environmental pollution and predicting its adverse health effects. Several health conditions, including chronic airway diseases such as asthma and chronic obstructive pulmonary disease, can be exacerbated by pollution. These diseases impose substantial health burdens with high morbidity and mortality. Recently, efforts have been made to develop digital technologies to alleviate such conditions. Moreover, the COVID-19 pandemic has facilitated the application of telemedicine and telemonitoring for patients with chronic airway diseases. This article reviews current trends and studies in digital technology utilization for investigating and managing environmental exposure and chronic airway diseases. First, we discussed the recent progression of digital technologies in general environmental healthcare. Then, we summarized the capacity of digital technologies in predicting exacerbation and self-management of airway diseases. Concluding these reviews, we provided suggestions to improve digital health technologies’ abilities to reduce the adverse effects of environmental exposure in chronic airway diseases, based on personal exposure-response modeling.

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“…To better understand these cause-and-effect relationships it is necessary to have access to spatially and temporally explicit information, preferably in the form of observations (certified measurements from air quality stations) of pollutants. However, due to the limited number and unrepresentative character of air quality stations [38], alternative information must be used, such as pollution concentration/deposition provided by air pollution chemical transport models [23]. These models take into account meteorological conditions (e.g., wind direction and speed, precipitation) as well as the main known emission sources at different heights, and simulate chemical reactions in the atmosphere, pollutant concentrations and deposition in different landcovers [30].…”

Section: Introductionmentioning

confidence: 99%

GAPS: Geo Data Portals for Air Pollution Studies

Fernandes¹,

Serrano²,

Oliveira³

et al. 2021

Preprint

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There is a wealth of data on air pollution within several users' reach, including modelled concentrations and depositions as well as observations from air quality stations. However, data integration to perceive spatial and temporal trends at the national level is a complex undertaking. The difficulties are mainly related to the data sources (many files with a lot of information and in different formats). In addition, the processing of this data is timeconsuming and impractical when the time period under analysis increases. Furthermore, the processing of spatial-temporal data requires the use of multiple specific tools, such as geographic information systems software and statistical, which are not readily accessible to non-specialised users. The proposed solution is to develop libraries that are responsible for aggregating different types of data related with pollution. In addition to allowing the integration of different data, the libraries are also the basis for developing web applications. The libraries allow the collection of data made available by the Portuguese Environment Agency (APA) and official modelling results for Europe. The applications developed will extend the libraries to allow data processing and representation through Dashboards. These Dashboards provide access to non-specialised users, namely regarding the trends of pollutants in each region. The implemented libraries allow the development of projects that would need access to the same data sources. These Dashboards allow for simplified access to data and for studies that have so far been beyond the reach of researchers.

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Representativeness of an air quality monitoring station for PM2.5 and source apportionment over a small urban domain

Cited by 28 publications

References 27 publications

Chemical Composition and Source Apportionment of Wintertime Airborne PM2.5 in Changchun, Northeastern China

Chemical Composition and Source Apportionment of Wintertime Airborne PM2.5 in Changchun, Northeastern China

Digital Healthcare for Airway Diseases from Personal Environmental Exposure

GAPS: Geo Data Portals for Air Pollution Studies

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