Estimation of Source Apportionment for Filter-based PM2.5 Data using the EPA-PMF Model at Air Pollution Monitoring Supersites

Abstract. While several studies have investigated winter-time air pollution with a wide range of concentration levels, hardly any results are available for longer time periods covering several winter-smog episodes at various locations; e.g. often only a few weeks from a single winter are investigated. Here, we present source apportionment results of winter-smog episodes from 16 air pollution monitoring stations across Switzerland from five consecutive winters. Radiocarbon (14C) analyses of the elemental (EC) and organic (OC) carbon fractions, as well as levoglucosan, major water-soluble ionic species and gas-phase pollutant measurements were used to characterize the different sources of PM10. The most important contributions to PM10 during winter-smog episodes in Switzerland were on average the secondary inorganic constituents (sum of nitrate, sulfate and ammonium = 41 ± 15%) followed by organic matter OM (30 ± 12%) and EC (5 ± 2%). The non-fossil fractions of OC (fNF,OC) ranged on average from 69–85% and 80–95 % for stations north and south of the Alps, respectively, showing that traffic contributes on average only up to ~30% to OC. The non-fossil fraction of EC (fNF,EC), entirely attributable to primary biomass burning, was on average 42 ± 13% and 49 ± 15% for north and south of the Alps, respectively. While a high correlation was observed between fossil EC and nitrogen oxides, both primarily emitted by traffic, these species did not significantly correlate with fossil OC (OCF), which seems to suggest that a considerable amount of OCF is secondary, formed from fossil precursors. Elevated fNF,EC and fNF,OC values and the high correlation of the latter with other wood burning markers, including levoglucosan and water soluble potassium (K+) indicate that biomass burning is the major source of carbonaceous aerosols during winter-smog episodes in Switzerland. The inspection of the non-fossil OC and EC levels and the relation with levoglucosan and water-soluble K+ shows different ratios for stations north and south of the Alps, most likely because of differences in burning technologies, for these two regions in Switzerland.

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Radiocarbon analysis of elemental and organic carbon in Switzerland during winter-smog episodes from 2008 to 2012 – Part 1: Source apportionment and spatial variability

Atmospheric Chemistry and Physics ◽

10.5194/acp-14-13551-2014 ◽

2014 ◽

Vol 14 (24) ◽

pp. 13551-13570 ◽

Cited By ~ 62

Author(s):

P. Zotter ◽

V. G. Ciobanu ◽

Y. L. Zhang ◽

I. El-Haddad ◽

M. Macchia ◽

...

Keyword(s):

Air Pollution ◽

Source Apportionment ◽

Water Soluble ◽

Pollution Monitoring ◽

Carbonaceous Aerosols ◽

Air Pollution Monitoring ◽

Wood Burning ◽

Wide Range ◽

The Alps ◽

North And South

Abstract. While several studies have investigated winter-time air pollution with a wide range of concentration levels, hardly any results are available for longer time periods covering several winter-smog episodes at various locations; e.g., often only a few weeks from a single winter are investigated. Here, we present source apportionment results of winter-smog episodes from 16 air pollution monitoring stations across Switzerland from five consecutive winters. Radiocarbon (14C) analyses of the elemental (EC) and organic (OC) carbon fractions, as well as levoglucosan, major water-soluble ionic species and gas-phase pollutant measurements were used to characterize the different sources of PM10. The most important contributions to PM10 during winter-smog episodes in Switzerland were on average the secondary inorganic constituents (sum of nitrate, sulfate and ammonium = 41 ± 15%) followed by organic matter (OM) (34 ± 13%) and EC (5 ± 2%). The non-fossil fractions of OC (fNF,OC) ranged on average from 69 to 85 and 80 to 95% for stations north and south of the Alps, respectively, showing that traffic contributes on average only up to ~ 30% to OC. The non-fossil fraction of EC (fNF,EC), entirely attributable to primary wood burning, was on average 42 ± 13 and 49 ± 15% for north and south of the Alps, respectively. While a high correlation was observed between fossil EC and nitrogen oxides, both primarily emitted by traffic, these species did not significantly correlate with fossil OC (OCF), which seems to suggest that a considerable amount of OCF is secondary, from fossil precursors. Elevated fNF,EC and fNF,OC values and the high correlation of the latter with other wood burning markers, including levoglucosan and water soluble potassium (K+) indicate that residential wood burning is the major source of carbonaceous aerosols during winter-smog episodes in Switzerland. The inspection of the non-fossil OC and EC levels and the relation with levoglucosan and water-soluble K+ shows different ratios for stations north and south of the Alps (most likely because of differences in burning technologies) for these two regions in Switzerland.

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A COMPARATIVE ANALYSIS AND REVIEW OF VARIOUS AIR POLLUTION MONITORING SCHEMES

i-manager’s Journal on Embedded Systems ◽

10.26634/jes.5.1.8249 ◽

2016 ◽

Vol 5 (1) ◽

pp. 30

Author(s):

HASAN MOHD. TAHSEENUL ◽

CHOURASIA VIJAY S. ◽

ASUTKAR SANJAY M. ◽

◽

...

Keyword(s):

Air Pollution ◽

Comparative Analysis ◽

Pollution Monitoring ◽

Air Pollution Monitoring

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H2020 project CAPTOR dataset: raw data collected by low-cost MOX ozone sensors in a real air pollution monitoring network.

Data in Brief ◽

10.1016/j.dib.2021.107127 ◽

2021 ◽

pp. 107127

Author(s):

Jose M. Barcelo-Ordinas ◽

Pau Ferrer-Cid ◽

Jorge Garcia-Vidal ◽

Mar Viana ◽

Ana Ripoll

Keyword(s):

Air Pollution ◽

Low Cost ◽

Monitoring Network ◽

Pollution Monitoring ◽

Raw Data ◽

Air Pollution Monitoring

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Research on air pollution system based on neural network

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189464 ◽

2020 ◽

pp. 1-11

Author(s):

Zhiqi Jiang ◽

Xidong Wang

Keyword(s):

Neural Network ◽

Air Pollution ◽

Image Features ◽

Air Pollutant ◽

Pollution Monitoring ◽

System Model ◽

Process Data ◽

Deep Convolutional Neural Networks ◽

Air Pollution Monitoring ◽

Neural Network Algorithm

This paper conducts in-depth research and analysis on the commonly used models in the simulation process of air pollutant diffusion. Combining with the actual needs of air pollution, this paper builds an air pollution system model based on neural network based on neural network algorithm, and proposes an image classification method based on deep learning and Gaussian aggregation coding. Moreover, this paper proposes a Gaussian aggregation coding layer to encode image features extracted by deep convolutional neural networks. Learn a fixed-size dictionary to represent the features of the image for final classification. In addition, this paper constructs an air pollution monitoring system based on the actual needs of the air system. Finally, this article designs a controlled experiment to verify the model proposed in this article, uses mathematical statistics to process data, and scientifically analyze the statistical results. The research results show that the model constructed in this paper has a certain effect.

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IoT Enabled an Open Framework for Air Pollution Monitoring System

2020 5th International Conference on Information Technology Research (ICITR) ◽

10.1109/icitr51448.2020.9310838 ◽

2020 ◽

Author(s):

B.H. Sudantha ◽

Manchanayaka MALSK ◽

Nilantha Premakumara ◽

Chamani Shiranthika ◽

C. Premachandra ◽

...

Keyword(s):

Air Pollution ◽

Monitoring System ◽

Pollution Monitoring ◽

Air Pollution Monitoring ◽

Open Framework

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COVID-19 and Air Pollution: Measuring Pandemic Impact to Air Quality in Five European Countries

Atmosphere ◽

10.3390/atmos12030290 ◽

2021 ◽

Vol 12 (3) ◽

pp. 290

Author(s):

Akvilė Feiferytė Skirienė ◽

Žaneta Stasiškienė

Keyword(s):

Air Pollution ◽

Air Quality ◽

Social Services ◽

Pollution Monitoring ◽

Air Pollution Monitoring ◽

Rapid Spread ◽

Air Quality Improvement ◽

Environment Agency ◽

Pollution Emissions ◽

Air Pollution Emissions

The rapid spread of the coronavirus (COVID-19) pandemic affected the economy, trade, transport, health care, social services, and other sectors. To control the rapid dispersion of the virus, most countries imposed national lockdowns and social distancing policies. This led to reduced industrial, commercial, and human activities, followed by lower air pollution emissions, which caused air quality improvement. Air pollution monitoring data from the European Environment Agency (EEA) datasets were used to investigate how lockdown policies affected air quality changes in the period before and during the COVID-19 lockdown, comparing to the same periods in 2018 and 2019, along with an assessment of the Index of Production variation impact to air pollution changes during the pandemic in 2020. Analysis results show that industrial and mobility activities were lower in the period of the lockdown along with the reduced selected pollutant NO2, PM2.5, PM10 emissions by approximately 20–40% in 2020.

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