Multiscale analysis of the dynamic relationship between particulate matter (PM10) and meteorological parameters using CEEMDAN: A focus on “Godzilla” African dust event

Meteorological parameters play an important role in determining the prevalence of ambient particulate matter (PM) in the upper north of Thailand. Mae Hong Son is a province located in this region and which borders Myanmar. This study aimed to determine the relationships between meteorological parameters and ambient concentrations of particulate matter less than 10 µm in diameter (PM10) in Mae Hong Son. Parameters were measured at an air quality monitoring station, and consisted of PM10, carbon monoxide (CO), ozone (O3), and meteorological factors, including temperature, rainfall, pressure, wind speed, wind direction, and relative humidity (RH). Nine years (2009–2017) of pollution and climate data obtained from the Thai Pollution Control Department (PCD) were used for analysis. The results of this study indicate that PM10 is influenced by meteorological parameters; high concentration occurred during the dry season and northeastern monsoon seasons. Maximum concentrations were always observed in March. The PM10 concentrations were significantly related to CO and O3 concentrations and to RH, giving correlation coefficients of 0.73, 0.39, and −0.37, respectively (p-value < 0.001). Additionally, the hourly PM10 concentration fluctuated within each day. In general, it was found that the reporting of daily concentrations might be best suited to public announcements and presentations. Hourly concentrations are recommended for public declarations that might be useful for warning citizens and organizations about air pollution. Our findings could be used to improve the understanding of PM10 concentration patterns in Mae Hong Son and provide information to better air pollution measures and establish a warning system for the province.

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Investigation on particulate matter 2.5 (PM2.5) for the past two decades in the central part of Iran, using background meteorological parameters

Arabian Journal of Geosciences ◽

10.1007/s12517-021-08485-2 ◽

2021 ◽

Vol 14 (21) ◽

Author(s):

Farahnaz Fazel-Rastgar ◽

Venkataraman Sivakumar

Keyword(s):

Particulate Matter ◽

Meteorological Parameters ◽

The Past ◽

Particulate Matter 2.5

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Estimating Particulate Matter Concentrations from MODIS AOD Considering Meteorological Parameters Using Random Forest Algorithm

Environmental Challenges and Solutions - Spatial Modeling and Assessment of Environmental Contaminants ◽

10.1007/978-3-030-63422-3_29 ◽

2021 ◽

pp. 591-609

Author(s):

Eeshan Basu ◽

Chalantika Laha Salui

Keyword(s):

Particulate Matter ◽

Random Forest ◽

Meteorological Parameters ◽

Random Forest Algorithm ◽

Modis Aod

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Particle Size Distribution of Airborne Microorganisms and Pathogens during an Intense African Dust Event in the Eastern Mediterranean

Environmental Health Perspectives ◽

10.1289/ehp.10684 ◽

2008 ◽

Vol 116 (3) ◽

pp. 292-296 ◽

Cited By ~ 170

Author(s):

Paraskevi N. Polymenakou ◽

Manolis Mandalakis ◽

Euripides G. Stephanou ◽

Anastasios Tselepides

Keyword(s):

Particle Size ◽

Particle Size Distribution ◽

Size Distribution ◽

Eastern Mediterranean ◽

Dust Event ◽

Airborne Microorganisms ◽

African Dust

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Long-Term Performance Assessment of Low-Cost Atmospheric Sensors in the Arctic Environment

Sensors ◽

10.3390/s20071919 ◽

2020 ◽

Vol 20 (7) ◽

pp. 1919 ◽

Cited By ~ 2

Author(s):

Federico Carotenuto ◽

Lorenzo Brilli ◽

Beniamino Gioli ◽

Giovanni Gualtieri ◽

Carolina Vagnoli ◽

...

Keyword(s):

Particulate Matter ◽

Relative Humidity ◽

Air Temperature ◽

Meteorological Parameters ◽

Low Cost ◽

Absolute Error ◽

Atmospheric Composition ◽

The Arctic ◽

Svalbard Archipelago ◽

Arctic Environment

The Arctic is an important natural laboratory that is extremely sensitive to climatic changes and its monitoring is, therefore, of great importance. Due to the environmental extremes it is often hard to deploy sensors and observations are limited to a few sparse observation points limiting the spatial and temporal coverage of the Arctic measurement. Given these constraints the possibility of deploying a rugged network of low-cost sensors remains an interesting and convenient option. The present work validates for the first time a low-cost sensor array (AIRQino) for monitoring basic meteorological parameters and atmospheric composition in the Arctic (air temperature, relative humidity, particulate matter, and CO2). AIRQino was deployed for one year in the Svalbard archipelago and its outputs compared with reference sensors. Results show good agreement with the reference meteorological parameters (air temperature (T) and relative humidity (RH)) with correlation coefficients above 0.8 and small absolute errors (≈1 °C for temperature and ≈6% for RH). Particulate matter (PM) low-cost sensors show a good linearity (r2 ≈ 0.8) and small absolute errors for both PM2.5 and PM10 (≈1 µg m−3 for PM2.5 and ≈3 µg m−3 for PM10), while overall accuracy is impacted both by the unknown composition of the local aerosol, and by high humidity conditions likely generating hygroscopic effects. CO2 exhibits a satisfying agreement with r2 around 0.70 and an absolute error of ≈23 mg m−3. Overall these results, coupled with an excellent data coverage and scarce need of maintenance make the AIRQino or similar devices integrations an interesting tool for future extended sensor networks also in the Arctic environment.

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