Meteorological conditions for severe foggy haze episodes over north China in 2016–2017 winter

Li, Xin; Gao, Zhiqiu; Li, Yubin; Gao, Chloe Y.; Ren, Jingzheng; Zhang, Xiaoye

doi:10.1016/j.atmosenv.2018.11.042

Cited by 21 publications

(13 citation statements)

References 34 publications

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“…According to studies at home and abroad, there exist certain correlations between PM 10 and PM 2.5 [69][70][71][72]. In order to fully understand the relationship between PM 2.5 and other major pollutants in Shanghai, we have calculated the ratio of PM 2.5 /PM 10 , PM 2.5 /SO 2 , PM 2.5 /NO 2 , PM 2.5 /CO, and PM 2.5 /O 3 , according to the 2017 Shanghai Environmental Bulletin [26] and the Shanghai Air Quality Monthly Report from January-October 2018 [73]. The results showed that the variation range of PM 2.5 /PM 10 was [0.4-0.7], while the ratio of PM 2.5 /SO 2 , PM 2.5 /NO 2 , PM 2.5 /CO, and PM 2.5 /O 3 was low (see Figure 2).…”

Section: Discussionmentioning

confidence: 99%

Section: Of 18mentioning

confidence: 99%

“…Li et al (2019) studied the meteorological conditions of the severe haze weather that frequently occurred in North China and concluded two main reasons for the decrease in visibility [26]. The first is the influence of meteorological conditions such as atmospheric currents, and the second is the change in the average astigmatism coefficient caused by the absorption and scattering of light due to fine particles and major air pollutants [26]. Golly et al [27] (2019) conducted experiments on the chemical characterization of PM 2.5 particles in five rural areas of France, and conducted chemical analysis on the samples every 6 days, including their organic carbon (OC), elemental carbon (EC), ion species, etc.…”

Section: Of 18mentioning

confidence: 99%

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Primary Pollutants and Air Quality Analysis for Urban Air in China: Evidence from Shanghai

Yan

Sun

et al. 2019

Sustainability

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In recent years, China's urban air pollution has caused widespread concern in the academic world. As one of China's economic and financial centers and one of the most densely populated cities, Shanghai ranks among the top in China in terms of per capita energy consumption per unit area. Based on the Shanghai Energy Statistical Yearbook and Shanghai Air Pollution Statistics, we have systematically analyzed Shanghai’s atmospheric pollutants from three aspects: Primary pollutants, pollutants changing trends, and fine particulate matter. The comprehensive pollution index analysis method, the grey correlation analysis method, and the Euclid approach degree method are used to evaluate and analyze the air quality in Shanghai. The results have shown that Shanghai's primary pollutants are PM2.5 and O3, and the most serious air pollution happens during the first half of the year, particularly in the winter. This is because it is the peak period of industrial energy use, and residential heating will also lead to an increase in energy consumption. Furthermore, by studying the particulate pollutants of PM2.5 and PM10, we clearly disclosed the linear correlation between PM2.5 and PM10 concentrations in Shanghai which varies seasonally.

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Section: Discussionmentioning

confidence: 99%

Section: Of 18mentioning

confidence: 99%

Section: Of 18mentioning

confidence: 99%

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Primary Pollutants and Air Quality Analysis for Urban Air in China: Evidence from Shanghai

Yan

Sun

et al. 2019

Sustainability

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“…In addition, haze is affected by meteorological factors such as weather that is not conducive to the spread of pollutants, and when pollutants accumulate under static weather conditions, haze is readily formed. Secondly, meteorological factors, the static wind in the horizontal direction and the inverse temperature in the vertical direction cause pollutants to gather and lead to the formation of haze weather (Li et al 2019;Wu et al 2016).…”

Section: Evaluation and Sensitivity Analysis Of The Ecosystem Servicementioning

confidence: 99%

Evaluation and sensitivity analysis of the ecosystem service functions of haze absorption by green space based on its quality in China

Ping¹,

Wang²,

Yang³

et al. 2020

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Evaluation of the ecosystem service functions of haze absorption by green space is important for controlling haze. In this study, the ecosystem service functions of haze absorption by green space in China in 2001, 2004, 2007, 2010, 2013, 2016 and 2018 are analyzed based on green space quality and sensitivity using a geographic information system (GIS) and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. The results showed that the benchmark ecosystem service functions of haze absorption by green space when considering only the area of green space showed a trend that increases first and then decreases in 2001–2018, with 9000458.55 million Kg, 9145110.75 million Kg and 7734526.75 million Kg in 2001, 2013 and 2018, respectively. However, the corrected functions based on green space quality were 7724215.34 million Kg, 8320301.79 million Kg and 6510132.55 million Kg in the corresponding years. This indicated large differences between ecosystem service functions of haze absorption based on the quality and area of green space; only considering the area of green space to evaluate ecosystem service functions will result in overestimation. In terms of the spatial distribution of the ecosystem service functions of haze absorption by green space, there were greater differences in the benchmark and corrected functions, and the spatial distributions of the maximum, intermediate and minimum ecosystem service functions were notably different. However, the benchmark and corrected functions all showed a consistent trend in the rank of their contribution rates and ecosystem service functions as well as consistent distribution trends: the spatial distribution of ecosystem service functions of haze absorption by green space was very different in the same year, but there was little difference among different years. The change coefficients for the ecosystem service functions of haze absorption by arable land and grass land remained stable, whereas the coefficient of sensitivity for forest cover was elastic. Patch density (PD) and the ecosystem service functions of SO2 absorption, NOx absorption, dust retention and total ecosystem services showed a significant negative correlation, with correlation coefficients of -0.407, -0.511, -.330 and -0.332, respectively. In contrast, the area-weighted mean shape index (SAPE_AM) and ecosystem service functions exhibited significant positive relationships with correlation coefficients of 0.650, 0.634, 0.568 and 0.570, respectively. The results provide an improved method for evaluating the ecosystem service functions of haze absorption by green space as well as a reference for the prevention and control of haze and the coordinated development of regional societies, the economy and the environment.

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“…CC BY 4.0 License. could vary up to several times, due to meteorological changes with the same emission sources (Zhang et al, 2010;Xing et al, 2011;Zheng et al, 2015;Cai et al, 2017;Liu et al, 2017;Ning et al, 2018;Yang et al, 2018;Li et al, 2019b). For example , Xing et al (2011)…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of the Surface Ozone and Fine Particulate Matter to Meteorological Parameters in China

Shi¹,

Hua²,

Li³

et al. 2020

Preprint

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Abstract. Meteorological conditions play important roles in the formation of ozone (O3) and fine particulate matter (PM2.5). China has been suffering from serious regional air pollution problems, characterized by high concentrations of surface O3 and PM2.5. In this study, the Community Multiscale Air Quality (CMAQ) model was used to quantify the sensitivity of surface O3 and PM2.5 to key meteorological parameters in different regions of China. Six meteorological parameters were perturbed to create different meteorological conditions, including temperature (T), wind speed (WS), absolute humidity (AH), planetary boundary layer height (PBLH), cloud liquid water content (CLW) and precipitation (PCP). Air quality simulations under the perturbed meteorological conditions were conducted in China in January and July of 2013. The changes in O3 and PM2.5 concentrations due to individual meteorological parameters were then quantified. T has the greatest impact on the daily maximum 8-h average O3 (O3-8 h) concentrations, which leads to O3-8 h increases by 1.7 ppb K−1 in January in Chongqing and 1.1 ppb K−1 in July in Beijing. WS, AH, and PBLH have a smaller but notable influence on O3-8 h with maximum change rates of 0.3, −0.15, and 0.14 ppb %−1, respectively. T, WS, AH, and PBLH have important effects on PM2.5 formation of in both January and July. In general, PM2.5 sensitivities are negative to T, WS, and PBLH and positive to AH in most regions of China. The sensitivities in January are much larger than in July. PM2.5 sensitivity to T, WS, PBLH, and AH in January can be up to −5 μg m−3 K−1, −3 μg m3 %−1, −1 g m−3, and +0.6 μg m−3 %−1, respectively, and in July can be up to −2 μg m−3 K−1, −0.4 μg m−3 %−1, −0.14 μg m−3 %−1, and +0.3 μg m−3 %−1, respectively. Other meteorological factors (CLW and PCP) have negligible effects on O3-8 h (less than 0.01 ppb %−1) and PM2.5 (less than 0.01 μg m−3 %−1). The results suggest that surface O3 and PM2.5 concentrations can change significantly due to changes in meteorological parameters and it is necessary to consider these effects when developing emission control strategies in different regions of China.

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Meteorological conditions for severe foggy haze episodes over north China in 2016–2017 winter

Cited by 21 publications

References 34 publications

Primary Pollutants and Air Quality Analysis for Urban Air in China: Evidence from Shanghai

Primary Pollutants and Air Quality Analysis for Urban Air in China: Evidence from Shanghai

Evaluation and sensitivity analysis of the ecosystem service functions of haze absorption by green space based on its quality in China

Sensitivity Analysis of the Surface Ozone and Fine Particulate Matter to Meteorological Parameters in China

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