Characteristics of Ultrafine Particles and Their Relationships with Meteorological Factors and Trace Gases in Wuhan, Central China

Zhang, Tianhao; Zhu, Zhenfeng; Gong, Wei; Xiang, Hao; Cui, Zhenzhen

doi:10.3390/atmos7080096

Cited by 11 publications

(3 citation statements)

References 37 publications

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“…In winter, China suffers severe environmental pollution by fine particles, with related values in most regions maintaining over 60 μg/m 3 , exceeding the recommended standards by approximately 58% or 300%, respectively. A major source of PM 2.5 in winter was probably winter heating, while the winter meteorological conditions impeded the diffusion of atmospheric pollution to a certain extent [42,43,44,45,46]. The highest values of PM 2.5 arose in the Xinjiang Autonomous Region, followed by the Jing-Jin-Ji Region (including Beijing, Tianjin, and Hebei) and Central China (including Hunan, Hubei, and Henan).…”

Section: Resultsmentioning

confidence: 99%

Real-Time Estimation of Satellite-Derived PM2.5 Based on a Semi-Physical Geographically Weighted Regression Model

Zhang

Liu

Zhu

et al. 2016

IJERPH

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The real-time estimation of ambient particulate matter with diameter no greater than 2.5 μm (PM2.5) is currently quite limited in China. A semi-physical geographically weighted regression (GWR) model was adopted to estimate PM2.5 mass concentrations at national scale using the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth product fused by the Dark Target (DT) and Deep Blue (DB) algorithms, combined with meteorological parameters. The fitting results could explain over 80% of the variability in the corresponding PM2.5 mass concentrations, and the estimation tends to overestimate when measurement is low and tends to underestimate when measurement is high. Based on World Health Organization standards, results indicate that most regions in China suffered severe PM2.5 pollution during winter. Seasonal average mass concentrations of PM2.5 predicted by the model indicate that residential regions, namely Jing-Jin-Ji Region and Central China, were faced with challenge from fine particles. Moreover, estimation deviation caused primarily by the spatially uneven distribution of monitoring sites and the changes of elevation in a relatively small region has been discussed. In summary, real-time PM2.5 was estimated effectively by the satellite-based semi-physical GWR model, and the results could provide reasonable references for assessing health impacts and offer guidance on air quality management in China.

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Section: Resultsmentioning

confidence: 99%

Real-Time Estimation of Satellite-Derived PM2.5 Based on a Semi-Physical Geographically Weighted Regression Model

Zhang

Liu

Zhu

et al. 2016

IJERPH

Self Cite

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“…The OMI NO 2 algorithm could compute accurate vertical column densities from NO 2 slant column densities, retrieved by spectral fitting. Since NO 2 is a short-life trace gas and has a tight relation to anthropogenic emissions and energy consumption [47,48,49,50], we utilized the satellite-derived NO 2 molar concentration as a simplified proxy of anthropogenic emissions.…”

Section: Methodsmentioning

confidence: 99%

Ground Level PM2.5 Estimates over China Using Satellite-Based Geographically Weighted Regression (GWR) Models Are Improved by Including NO2 and Enhanced Vegetation Index (EVI)

Zhang

Gong

Wang

et al. 2016

IJERPH

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Highly accurate data on the spatial distribution of ambient fine particulate matter (<2.5 μm: PM2.5) is currently quite limited in China. By introducing NO2 and Enhanced Vegetation Index (EVI) into the Geographically Weighted Regression (GWR) model, a newly developed GWR model combined with a fused Aerosol Optical Depth (AOD) product and meteorological parameters could explain approximately 87% of the variability in the corresponding PM2.5 mass concentrations. There existed obvious increase in the estimation accuracy against the original GWR model without NO2 and EVI, where cross-validation R2 increased from 0.77 to 0.87. Both models tended to overestimate when measurement is low and underestimate when high, where the exact boundary value depended greatly on the dependent variable. There was still severe PM2.5 pollution in many residential areas until 2015; however, policy-driven energy conservation and emission reduction not only reduced the severity of PM2.5 pollution but also its spatial range, to a certain extent, from 2014 to 2015. The accuracy of satellite-derived PM2.5 still has limitations for regions with insufficient ground monitoring stations and desert areas. Generally, the use of NO2 and EVI in GWR models could more effectively estimate PM2.5 at the national scale than previous GWR models. The results in this study could provide a reasonable reference for assessing health impacts, and could be used to examine the effectiveness of emission control strategies under implementation in China.

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“…A previous study explored the mean seasonal behavior of ultrafine particles (15-660 nm) and its correlation with precipitation [9]. Another study examined the behavior of fine and coarse particles under diverse meteorological conditions [10], showing that 1.6 mm of rainfall significantly reduced PM2.5 levels.…”

Section: Introductionmentioning

confidence: 99%

Impact of Rain Precipitation on Urban Atmospheric Particle Matter Measured at Three Locations in France between 2013 and 2019

2021

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As atmospheric particle matter (PM) pollution has been proven to be a public health risk, we investigated how PM concentrations of various particle diameters may be impacted by precipitation. Repeated measures over time of urban PM concentrations for particles of 0.2–50 µm in diameter were compared with precipitation data from Météo-France weather stations in Paris, Angers and Palaiseau from 2013 to 2019. A significant negative correlation, using Kendall’s rank correlation, was found between the amount of precipitation and concentrations of particles >3 µm. Distribution comparative analysis (Dunn’s test) of 154 events of 1 mm or more of rain demonstrated a decrease in concentrations for particles from 10 to 50 µm in diameter. Additionally, granulometric analysis of a typical heavy rain event showed a 10-fold decrease in concentrations of particles 10 to 30 µm in diameter one hour after rain compared with one hour before. We were able to show that measured concentrations of particles between 10 and 50 µm in diameter diminish when it rains, with a lasting effect of approximately 10–15 h.

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Characteristics of Ultrafine Particles and Their Relationships with Meteorological Factors and Trace Gases in Wuhan, Central China

Cited by 11 publications

References 37 publications

Real-Time Estimation of Satellite-Derived PM2.5 Based on a Semi-Physical Geographically Weighted Regression Model

Real-Time Estimation of Satellite-Derived PM2.5 Based on a Semi-Physical Geographically Weighted Regression Model

Ground Level PM2.5 Estimates over China Using Satellite-Based Geographically Weighted Regression (GWR) Models Are Improved by Including NO2 and Enhanced Vegetation Index (EVI)

Impact of Rain Precipitation on Urban Atmospheric Particle Matter Measured at Three Locations in France between 2013 and 2019

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