Insight into PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt; sources by applying positive matrix factorization (PMF) at urban and rural sites of Beijing

Srivastava, Deepchandra; Xu, Jingsha; Vu, Tuan V.; Liu, Di; Fu, Pingqing; Hou, Siqi; Palmerola, Natalia Moreno; Shi, Zongbo; Harrison, Roy M.

doi:10.5194/acp-21-14703-2021

Cited by 51 publications

(15 citation statements)

References 118 publications

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“…Particulate matter (PM) in the atmosphere has attracted much attention due to its impact on air quality, human health, and climate change. − A high-performance method to analyze the source and formation mechanism of PM is a prerequisite for controlling and reducing its pollution. However, existing source resolution methods such as in situ observations combined with receptor modeling (e.g., positive matrix factorization (PMF)) are uncertain and time- and labor-intensive. − Therefore, a simple, fast, and explicit method is needed to analyze the sources of particulate matter.…”

Section: Introductionmentioning

confidence: 99%

Facile Differentiation of Four Sources of Water-Soluble Organic Carbon in Atmospheric Particulates Using Multiple Fluorescence Spectral Fingerprints

Tan

Qin

et al. 2022

Environ. Sci. Technol. Lett.

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Water-soluble organic carbon (WSOC) is an important component of atmospheric particulate matter, consisting of complex and reactive organics with significant environmental impacts such as light absorption and health risks. This study proposed a source identification method for particulate matter based on the excitation–emission matrix (EEM) multiple fluorescence spectral fingerprints of WSOC. It was found that EEM fluorescence fingerprints, including fluorescence spectra and fluorescence parameters, could recognize differences in chemical structure characteristics of WSOC from diverse sources involving biomass burning, coal combustion, dust emission, and vehicle emission. Applying statistical methods such as Bootstrap sampling and the Mann–Whitney U test, a simple guide to determine the pollution source of particulate matter was established based on discrepancies in fluorescence fingerprints such as the modified regional fluorescence integral and fluorescence quantum yield. Fourier-transform infrared spectroscopy and ultrahigh-performance liquid chromatography–mass spectrometry combined with parallel factor analysis showed that the diversity in EEMs of different source samples was attributed to aromatic groups with various side chains and substituents. This study will provide a simple and intuitive potential approach for the source apportionment of atmospheric particulate matter and set the stage for studying the relationship between atmospheric chemical processes and fluorescent components.

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Section: Introductionmentioning

confidence: 99%

Facile Differentiation of Four Sources of Water-Soluble Organic Carbon in Atmospheric Particulates Using Multiple Fluorescence Spectral Fingerprints

Tan

Qin

et al. 2022

Environ. Sci. Technol. Lett.

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“…This receptor model developed by Paatero and Tapper [36] is now widely recognized and accepted as a data analysis tool. This model has an extensive range of applications, since it is frequently employed to study the sources of Particulate Matter (both as PM 10 and PM 2.5 ) [37], and also to VOCs data [38,39].…”

Section: Statistical Treatment On the Dataset By Pmf Modelmentioning

confidence: 99%

Air Quality Assessment in the Central Mediterranean Sea (Tyrrhenian Sea): Anthropic Impact and Miscellaneous Natural Sources, including Volcanic Contribution, on the Budget of Volatile Organic Compounds (VOCs)

et al. 2021

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The results of air pollution assessment during a 2017 cruise of the research ship “Minerva Uno” in the Tyrrhenian Sea are reported. Volatile Organic Compounds (VOCs), Oxygenated Volatile Organic Compounds (OVOCs), and pollutants such as nitrogen oxides, ozone, and sulphur dioxide were monitored throughout the cruise. The shallow waters at ten sites of the investigated area were also analyzed. Organic compounds such as n-alkanes showed a bimodal distribution with a maximum at C5–C6 and C10–C11 at sites the most affected by anthropic impact, whereas remote sites showed a unimodal distribution with maximum at C10–C11. The most abundant atmospheric OVOC was acetone (3.66 μg/m3), accounting for 38%; formaldehyde (1.23 μg/m3) and acetaldehyde (0.99 μg/m3) made up about 22–29% of the total. The influence of some natural sources as volcanoes, in the southern part of the Tyrrhenian Sea near the Aeolian arc was studied. This source did not induce any noticeable effect on the total amount of hydrocarbons nor on the levels of trace gases such as CFCs, whereas the trends of sulphur dioxide seemed to confirm a possible contribution. The impact of underwater emissions was observed near the Panarea and Vulcano islands, where lower pHs, high levels of Fe and Mn, and diagnostic of vent activity, were measured.

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“…Chemical speciation data was used in this work to estimate and identify possible PM2.5 sources using the PMF model. (Srivastava et al 2021) used PMF modelling in urban and rural areas of Beijing, China. One of the major limitations of these studies using PMF models is that they have used chemical-based analysis of PM2.5 with minimum sample points in small geographical areas.…”

Section: Introductionmentioning

confidence: 99%

Sources, Pattern, and Health Impacts of PM2.5 in the Central Region of Bangladesh using PMF, SOM, and Machine Learning Techniques

Bhuiyan

Iqbal

Rahman

2022

Preprint

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The Particulate Matter 2.5 (PM2.5) is one of the major environmental and public health threats in Bangladesh. It is important to explore the relationship between PM2.5, and other variables to mitigate its adverse health impacts. This study aims to understand the sources, patterns, and health impacts of PM2.5 in five central districts of Bangladesh using fourteen variables. These variables have been analyzed by PMF, SOM, Machine Learning, and Multi-regression analysis. This paper has found that PM2.5 is correlated positively with NO (0.55), BC (0.45), CH4 (0.38), and NOx (0.22), while correlated negatively with Rainfall (-0.10), CO (-0.33), and SO2 (-0.24). In PMF modeling, the R2 values of settlement density (1.00), SO2 (0.99), DEM (0.94), Rainfall (0.77), NO (0.74) and Brickfield (0.66) have found as the most correlated variables. In this study, the dominant variables NO, CO, Rainfall, O3, AOT, CH4, and BC are found in Factor 1; SO2, settlement density, and DEM are found in Factor 2; and population density and brickfield are found in Factor 3. In SOM mapping, most of the variables are concentrated in the north-eastern, central, and south-eastern parts of the study area. The prediction of PM2.5 using machine learning is significant, showing reasonable R2 for Random Forest (0.85), Extreme gradient boosting (0.81), and Stepwise Linear (0.76). The impact of PM2.5 on child ARI is significant (p = 0.002, R2 = 0.75); while child mortality is not significant (p = 0.268; R2 = 0.55). These results will be useful for creating and implementing local and regional PM2.5 mitigation plans. Concern institutions and academia may also use these outputs for reducing health impacts, particularly child mortality and acute respiratory infections.

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Insight into PM<sub>2.5</sub> sources by applying positive matrix factorization (PMF) at urban and rural sites of Beijing

Cited by 51 publications

References 118 publications

Facile Differentiation of Four Sources of Water-Soluble Organic Carbon in Atmospheric Particulates Using Multiple Fluorescence Spectral Fingerprints

Facile Differentiation of Four Sources of Water-Soluble Organic Carbon in Atmospheric Particulates Using Multiple Fluorescence Spectral Fingerprints

Air Quality Assessment in the Central Mediterranean Sea (Tyrrhenian Sea): Anthropic Impact and Miscellaneous Natural Sources, including Volcanic Contribution, on the Budget of Volatile Organic Compounds (VOCs)

Sources, Pattern, and Health Impacts of PM2.5 in the Central Region of Bangladesh using PMF, SOM, and Machine Learning Techniques

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