2021
DOI: 10.3390/atmos12070870
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Characterization of Black Carbon and Its Correlations with VOCs in the Northern Region of Hangzhou Bay in Shanghai, China

Abstract: Ozone and PM2.5 (all particulate matter with diameter of 2.5 µm or smaller) are currently two disturbing environmental issues in most cities of China. Black carbon (BC), mainly from incomplete combustion, is one of the most important components of PM2.5 because it can absorb light and contribute to haze pollution and global warming. Meanwhile, volatile organic compounds (VOCs) have become a major air pollutant due to their association with haze, ozone (O3), global warming and human health by direct or indirect… Show more

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Cited by 4 publications
(2 citation statements)
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“…Benzene emissions are more concentrated towards chemical processes (chemical industry, brick kiln, smoking etc.). A positive correlation between the two can signify the possible contribution from surrounding chemical industries/brick kilns to the BC mass concentration [75] . Mostly brick kilns are in proximity to the study sites.…”
Section: Bc Aerosols and Benzenementioning
confidence: 99%
“…Benzene emissions are more concentrated towards chemical processes (chemical industry, brick kiln, smoking etc.). A positive correlation between the two can signify the possible contribution from surrounding chemical industries/brick kilns to the BC mass concentration [75] . Mostly brick kilns are in proximity to the study sites.…”
Section: Bc Aerosols and Benzenementioning
confidence: 99%
“…Nitrogen dioxide (NO 2 ), one stable gaseous pollutant among NO x , cannot only form nitric acid rain when reacting with water (H 2 O) but also produces ozone (O 3 ) under ultraviolet wavelengths in cloudless skies [21]. NO 2 studies in China's marginal seas mostly adopted fixed instrument observations [22,23] or model simulations, such as WRF-Chem [24,25], and the experimental areas were mostly the South China Sea (SCS) [23,24], Bohai Sea [25] or Hangzhou Bay [22]. In recent years, machine learning (ML) algorithms (such as random forest) have been utilized to analyze and predict NO 2 with high accuracy [26].…”
Section: Introductionmentioning
confidence: 99%