A New Type of Haze? The December 2015 Purple (Magenta) Haze Event in Nanjing, China

Liu, Duanyang; Liu, Xuejun; Wang, Hongbin; Li, Yang; Kang, Zhiming; Cao, Lei; Yu, Xiangyao; Chen, Hao

doi:10.3390/atmos8040076

Cited by 6 publications

(6 citation statements)

References 22 publications

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“…In particular, at 21:00 BT on 4 December, the hourly concentration of PM 2.5 was up to 764 μg·m −3 , about 1-2 times higher than the other fog or haze events reported [44,45]. e situation was not improved until the midnight of 4 December, due to precipitation before the next fog occurrence.…”

Section: Heavy Pollutionmentioning

confidence: 75%

A Persistent Fog Event Involving Heavy Pollutants in Yancheng Area of Jiangsu Province

Zhu

Chengying

et al. 2018

Advances in Meteorology

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In the early December 2013, dense fog involving heavy pollutants lasted for 9 days in the Yancheng area. e characteristics, formation, and lasting mechanisms of this persistent fog were analyzed based on observational data at the Sheyang site, reanalysis data, and final analysis data from NCEP/NCAR, combining with the weather background and meteorological and physical variable fields. Results include that (1) the fog process was characterized by long duration, low visibility, and high pollutants concentration, (2) the atmospheric general circulation contributed to the sustainability and development of the heavily polluted fog, (3) deep inversion was the key thermal factor causing the heavily polluted fog, (4) the fog exhibited obvious outbreaks with good visibility weather turned to severe fog several times, and (5) the weak cold air invasion and radiative cooling were the triggering factors to the sudden enhancement of the fog.

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Section: Heavy Pollutionmentioning

confidence: 75%

A Persistent Fog Event Involving Heavy Pollutants in Yancheng Area of Jiangsu Province

Zhu

Chengying

et al. 2018

Advances in Meteorology

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“…Combined with the vertical profiles of the temperature (Figure 7b), it could be inferred that the temperature inversion layer formed between 500 and 600 m AGL. In most cases, the temperature inversion layers formed during a pollution event could restrain convection and favor the accumulation of particulate matter, thereby inhibiting the vertical diffusion of PM 2.5 [34]. Flights A3 and A4 also exhibited relative homogeneity in the PM 2.5 mass concentration as the altitude increased below 600 m AGL, and the intensity of the temperature inversion layer remained stable and strong.…”

Section: Flight Measurement Featuresmentioning

confidence: 99%

The Characteristics and Contributing Factors of Air Pollution in Nanjing: A Case Study Based on an Unmanned Aerial Vehicle Experiment and Multiple Datasets

et al. 2018

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Unmanned aerial vehicle (UAV) experiments, multiple datasets from ground-based stations and satellite remote sensing platforms, and backward trajectory models were combined to investigate the characteristics and influential mechanisms of the air pollution episode that occurred in Nanjing during 3–4 December 2017. Before the experiments, the position of the detector mounted on a UAV that was minimally disturbed by the rotation of the rotors was analyzed based on computational fluid dynamics (CFD) simulations. The combined analysis indicated that the surface meteorological conditions—high relative humidity, low wind speed, and low temperature—were conducive to the accumulation of PM2.5. Strongly intense temperature inversion layers and the low thickness of the atmospheric mixed layer could have resulted in elevated PM2.5 mass concentrations. In the early stage, air pollution was affected by the synoptic circulation of the homogenous pressure field and low wind speeds, and the pollutants mainly originated from emissions from surrounding areas. The aggravated pollution was mainly attributed to the cold front and strong northwesterly winds above 850 hPa, and the pollutants mostly originated from the long-distance transport of emissions with northwesterly winds, mainly from the Beijing‒Tianjin‒Hebei (BTH) region and its surrounding areas. This long-distance transport predominated during this event. The air pollution level and aerosol optical depth (AOD) were positively correlated with respect to their spatial distributions; they could reflect shifts in areas of serious pollution. Pollution was concentrated in Anhui Province when it was alleviated in Nanjing. Polluted dust, polluted continental and smoke aerosols were primarily observed during this process. In particular, polluted dust aerosols accounted for a major part of the transport stage, and existed between the surface and 4 km. Moreover, the average extinction coefficient at lower altitudes (<1 km) was higher for aerosol deposition.

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“…The experiment proved that the system could provide a reliable prediction. Liu et al [11,12] analyzed the fuchsia haze events in Nanjing in 2017. Singh, V. et al [13] proposed a method based on co-kriging to predict air pollution.…”

Section: Introductionmentioning

confidence: 99%

Haze Prediction Model Using Deep Recurrent Neural Network

et al. 2021

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In recent years, haze pollution is frequent, which seriously affects daily life and production process. The main factors to measure the degree of smoke pollution are the concentrations of PM2.5 and PM10. Therefore, it is of great significance to study the prediction of PM2.5/PM10 concentration. Since PM2.5 and PM10 concentration data are time series, their time characteristics should be considered in their prediction. However, the traditional neural network is limited by its own structure and has some weakness in processing time related data. Recurrent neural network is a kind of network specially used for sequence data modeling, that is, the current output of the sequence is correlated with the historical output. In this paper, a haze prediction model is established based on a deep recurrent neural network. We obtained air pollution data in Chengdu from the China Air Quality Online Monitoring and Analysis Platform, and conducted experiments based on these data. The results show that the new method can predict smog more effectively and accurately, and can be used for social and economic purposes.

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A New Type of Haze? The December 2015 Purple (Magenta) Haze Event in Nanjing, China

Cited by 6 publications

References 22 publications

A Persistent Fog Event Involving Heavy Pollutants in Yancheng Area of Jiangsu Province

A Persistent Fog Event Involving Heavy Pollutants in Yancheng Area of Jiangsu Province

The Characteristics and Contributing Factors of Air Pollution in Nanjing: A Case Study Based on an Unmanned Aerial Vehicle Experiment and Multiple Datasets

Haze Prediction Model Using Deep Recurrent Neural Network

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