Changes in visibility with PM2.5 composition and relative humidity at a background site in the Pearl River Delta region

Fu, Xiaogang; Wang, Xinming; Hu, Qihou; Li, Guanghui; Ding, Xiang; Zhang, Yanli; He, Quanfu; Liu, Tengyu; Zhang, Zhou; Yu, Qingqing; Shen, Ruqing; Bi, Xinhui

doi:10.1016/j.jes.2015.12.001

Cited by 68 publications

(26 citation statements)

References 56 publications

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“…On the other hand, He et al (2017) suggested that temperature and humidity were either positively or negatively correlated with PM 2.5 concentrations in different regions of China. In terms of humidity, when the humidity is low, PM 2.5 concentration increases with the increase in humidity due to hygroscopic increase in and accumulation of PM 2.5 (Fu et al, 2016). When the humidity continues to grow, the particles grow too heavy to stay in the air, leading to dry (particles drop to the ground; Wang and Ogawa, 2015) and wet deposition (precipitation; Li et al, 2015b), and the reduction of PM 2.5 concentrations.…”

Section: Discussionmentioning

confidence: 99%

Understanding meteorological influences on PM<sub>2.5</sub> concentrations across China: a temporal and spatial perspective

et al. 2018

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Abstract. With frequent air pollution episodes in China, growing research emphasis has been put on quantifying meteorological influences on PM 2.5 concentrations. However, these studies mainly focus on isolated cities, whilst meteorological influences on PM 2.5 concentrations at the national scale have not yet been examined comprehensively. This research employs the CCM (convergent cross-mapping) method to understand the influence of individual meteorological factors on local PM 2.5 concentrations in 188 monitoring cities across China. Results indicate that meteorological influences on PM 2.5 concentrations have notable seasonal and regional variations. For the heavily polluted North China region, when PM 2.5 concentrations are high, meteorological influences on PM 2.5 concentrations are strong. The dominant meteorological influence for PM 2.5 concentrations varies across locations and demonstrates regional similarities. For the most polluted winter, the dominant meteorological driver for local PM 2.5 concentrations is mainly the wind within the North China region, whilst precipitation is the dominant meteorological influence for most coastal regions. At the national scale, the influence of temperature, humidity and wind on PM 2.5 concentrations is much larger than that of other meteorological factors. Amongst eight factors, temperature exerts the strongest and most stable influence on national PM 2.5 concentrations in all seasons. Due to notable temporal and spatial differences in meteorological influences on local PM 2.5 concentrations, this research suggests pertinent environmental projects for air quality improvement should be designed accordingly for specific regions.

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Section: Discussionmentioning

confidence: 99%

Understanding meteorological influences on PM<sub>2.5</sub> concentrations across China: a temporal and spatial perspective

et al. 2018

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“…The ambient RH might be the most complex factor affecting PM 2.5 concentrations [42][43][44]. The PM 2.5 concentration and the RH were both highest in 2013, which suggests a possible contribution of high RH to a PM 2.5 concentration.…”

Section: Discussionmentioning

confidence: 99%

Contribution of Meteorological Conditions to the Variation in Winter PM2.5 Concentrations from 2013 to 2019 in Middle-Eastern China

Liu¹,

Wang²,

Shen³

et al. 2019

Atmosphere

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Severe air pollution events accompanied by high PM2.5 concentrations have been repeatedly observed in Middle-Eastern China since 2013 and decreased in recent years. The reason for this caused widespread attention. The month of January was selected to represent the winter season annual changes in the winter PM2.5 and meteorological conditions—including the upper-air meridional circulation index (MCI), winds at 700 and 850 hPa levels and surface meteorology—from 2013 to 2019. These conditions were analyzed to study the contribution of meteorology changing to the annual PM2.5 changing on the regional scale. Results show that, based on values of upper-level MCI, the years 2014, 2015, 2017, and 2019 were defined as meteorology-haze years and the years 2016 and 2018 were defined as meteorology-clean years. A change in meteorological conditions may lead to a 26% change in PM2.5 concentration between 2014 and 2013 (two meteorology-haze years) and 16–20% changes in PM2.5 concentration between meteorology-haze years and meteorology-clean years. Changes in pollutant emissions may cause 21–47% changes in PM2.5 concentration between each two meteorology-haze years. A comparison of two meteorology-clean years and pollutant emissions in 2018 may be reduced by 40% compared with 2016. Overall, changes in emissions had a greater influence on changes in PM2.5 compared with meteorological conditions.

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“…Extensive studies have been carried out to establish the quantitative relationship between PM 2.5 concentrations and atmospheric visibility, accounting for the ambient meteorological conditions. The relationship between PM 2.5 concentrations and visibility has the form of a power function in the JingJinJi and Pearl River Delta regions (X. Fu et al, ; P. Zhao et al, ), whereas this relationship can be described by an exponential function in Xi'an and at Tai Mountain (Cao et al, ; T. Zhao et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…The relationship between PM 2.5 concentrations and visibility has the form of a power function in the JingJinJi and Pearl River Delta regions (X. Fu et al, 2016;P. Zhao et al, 2011), whereas this relationship can be described by an exponential function in Xi'an and at Tai Mountain (Cao et al, 2012;.…”

Section: Introductionmentioning

confidence: 99%

The Effects of PM_2.5 Concentrations and Relative Humidity on Atmospheric Visibility in Beijing

2019

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Atmospheric visibility is often used as a proxy for ambient air quality. However, in addition to the concentrations of particulate matter, visibility is also affected by meteorological conditions. The relative contributions of PM2.5 concentrations and meteorological conditions to visibility are not yet clear. In this study, the individual contributions of PM2.5 concentrations and the relative humidity (RH) to the visibility are measured based on observations at 12 stations in Beijing. We find that visibility decreases quickly as PM2.5 concentrations increase at clean condition and decreases slowly when PM2.5 concentrations exceed 100 μg/m3. The visibility decreases linearly as PM2.5 concentrations increase when PM2.5 concentrations lower than 50 μg/m3; however, there tends to be an exponential relationship as PM2.5 concentrations increase. The PM2.5 concentrations can explain 50% of the variance in visibility in clean and high‐humidity environments, whereas this fraction decreases to 10–15% when the PM2.5 concentrations exceed 200 μg/m3. In contrast, RH has little effect on visibility under dry conditions. When the RH exceeds 40%, atmospheric visibility tends to display an inversely proportional and exponential relationship with RH under polluted and clean conditions, respectively. Under high‐humidity and highly polluted conditions, up to 40% of the variance in visibility is associated with RH. The PM2.5 concentrations dominate the variations in visibility under dry or low PM2.5 concentrations, and the contributions of RH become increasingly important as PM2.5 concentrations and humidity increase. The difference of aerosol types and weather conditions increase the uncertainties of correlation coefficients between visibility and PM2.5/RH. This study emphasizes the atmospheric conditions when employing visibility as a proxy for air pollution.

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Changes in visibility with PM2.5 composition and relative humidity at a background site in the Pearl River Delta region

Cited by 68 publications

References 56 publications

Understanding meteorological influences on PM<sub>2.5</sub> concentrations across China: a temporal and spatial perspective

Understanding meteorological influences on PM<sub>2.5</sub> concentrations across China: a temporal and spatial perspective

Contribution of Meteorological Conditions to the Variation in Winter PM2.5 Concentrations from 2013 to 2019 in Middle-Eastern China

The Effects of PM_2.5 Concentrations and Relative Humidity on Atmospheric Visibility in Beijing

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Changes in visibility with PM2.5 composition and relative humidity at a background site in the Pearl River Delta region

Cited by 68 publications

References 56 publications

Understanding meteorological influences on PM&lt;sub&gt;2.5&lt;/sub&gt; concentrations across China: a temporal and spatial perspective

Understanding meteorological influences on PM&lt;sub&gt;2.5&lt;/sub&gt; concentrations across China: a temporal and spatial perspective

Contribution of Meteorological Conditions to the Variation in Winter PM2.5 Concentrations from 2013 to 2019 in Middle-Eastern China

The Effects of PM2.5 Concentrations and Relative Humidity on Atmospheric Visibility in Beijing

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Understanding meteorological influences on PM<sub>2.5</sub> concentrations across China: a temporal and spatial perspective

Understanding meteorological influences on PM<sub>2.5</sub> concentrations across China: a temporal and spatial perspective

The Effects of PM_2.5 Concentrations and Relative Humidity on Atmospheric Visibility in Beijing