Spatial distribution and sources of winter black carbon and brown carbon in six Chinese megacities

Zhang, Qian; Shen, Zhenxing; Zhang, Tian; Kong, Shaofei; Lei, Yali; Wang, Qiyuan; Tao, Jun; Zhang, Renjian; Wei, Peng; Wei, Chong; Cui, Song; Cheng, Tiantao; Ho, Steven Sai Hang; Li, Ziyi; Xu, Hongmei; Cao, Junji

doi:10.1016/j.scitotenv.2020.143075

Cited by 49 publications

(24 citation statements)

References 68 publications

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“…S1). The contribution of secondary BrC in cloud INT and cloud-free particles are in the low range of reported values (as summarized in Table S2) (Gao et al, 2022;Kaskaoutis et al, 2021;Lin et al, 2021;Wang et al, 2019aWang et al, , 2019bWang et al, , 2020bWang et al, , 2021Zhang et al, 2020Zhang et al, , 2021Zhu et al, 2021). Differently, the contribution of secondary formed BrC to the total BrC light-absorption is 67-85% in the cloud RES particles, surprisingly higher than those in the cloud-free and cloud INT particles.…”

Section: The Optical Properties Of Brc During Cloud Eventsmentioning

confidence: 88%

The optical properties and in-situ observational evidence for the formation of brown carbon in cloud

Guo

Yang

et al. 2021

Preprint

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Abstract. Atmospheric brown carbon (BrC) makes a substantial contribution to aerosol light-absorbing and thus the global radiative forcing. Although BrC may change the lifetime of the cloud and ultimately affect precipitation, little is known regarding the optical properties and formation of BrC in the cloud. In the present study, the light-absorption properties of cloud droplet residual (cloud RES) were measured by coupled a ground-based counterflow virtual impactor (GCVI) and an Aethalometer (AE-33), in addition to the cloud interstitial (cloud INT) and ambient (cloud-free) particles by PM2.5 inlet-AE-33, at Mt. Tianjing (1690 m a.s.l.), a remote mountain site in southern China, from November to December 2020. Meanwhile, the light-absorption and fluorescence properties of water-soluble organic carbon (WSOC) in the collected cloud water and PM2.5 samples were also obtained, associated with the concentration of water-soluble ions. The mean light-absorption coefficient (Abs370) of the cloud RES, cloud INT, and cloud-free particles were 0.25 ± 0.15, 1.16 ± 1.14, and 1.47 ± 1.23 Mm−1, respectively. The Abs365 of WSOC was 0.11 ± 0.08 Mm−1 in cloud water and 0.40 ± 0.31 Mm−1 in PM2.5, and the corresponding mass absorption efficiency (MAE365) was 0.17 ± 0.07 and 0.31 ± 0.21 m2·g−1, respectively. A comparison of the light-absorption coefficient between BrC in the cloud RES/cloud INT and WSOC in cloud water/PM2.5 indicates a considerable contribution (48–75 %) of water-insoluble BrC to total BrC light-absorption. Secondary BrC estimated by minimum R squared (MRS) method dominated the total BrC in cloud RES (67–85 %), rather than in the cloud-free (11–16 %) and cloud INT (9–23 %) particles. It may indicate the formation of secondary BrC during cloud processing. Supporting evidence includes the enhanced WSOC and dominant contribution of secondary formation/biomass burning factor (> 80 %) to Abs365 in cloud water provided by Positive Matrix Factorization (PMF) analysis. In addition, we showed that the light-absorption of BrC in cloud water was closely related to humic-like substances and tyrosine/proteins-like substances (r > 0.63, p < 0.01), whereas only humic-like substances for PM2.5, as identified by excitation-emission matrix fluorescence spectroscopy.

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Section: The Optical Properties Of Brc During Cloud Eventsmentioning

confidence: 88%

The optical properties and in-situ observational evidence for the formation of brown carbon in cloud

Guo

Yang

et al. 2021

Preprint

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“…Therefore, PM 2.5 has become one of the most important targets for environmental pollution prevention and control in the world. There are differences in the PM 2.5 situation in different regions because factors such as climate and urban morphology will have an impact on the formation and dispersion of PM 2.5 [2]. It is very important to understand the spatial distribution and related dynamic changes of PM 2.5 , which is conducive to formulating effective measures to reduce and control the harm caused by PM 2.5 combined with the actual situation.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, Harbin, a typical cold-climate city, ranked among the top ten cities with the worst air quality in China, was taken as an experimental case. This study plans to achieve the following goals: (1) to illustrate the spatial and temporal distribution of PM 2.5 concentration in block scale; (2) to analyze the influence of urban microclimate on PM 2.5 concentration and the influence radius of urban morphology parameters; (3) to establish a prediction model of PM 2.5 concentration in urban blocks of cold climate based on the gradient boosted regression trees model and verify its effectiveness; (4) to study the influence degree of different urban morphologies on PM 2.5 concentration and give advice on urban planning for a better environment.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Effects of Urban Morphology Parameters on PM2.5 Distribution in Northeast China Based on Gradient Boosted Regression Trees Method

et al. 2022

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The dispersion of urban pollutants is affected by the urban morphology parameters. The objective of this study was to investigate the correlation between PM2.5 distribution and urban morphology parameters in a cold-climate city in China. Field measurements were performed to record the PM2.5 concentration and microclimate parameters at 25 points in a 10 km2 urban area in Harbin, China. It was found that the maximum difference of PM2.5 concentration among the measuring points at the same time could be up to 69.03 μg/m3. In this study, a geographic information system (GIS) was used to extract and screen the urban morphology parameter data under reasonable buffer radius, the gradient boosted regression trees model (GBRT) was used to carry out the prediction experiment of PM2.5 concentration and explore the nonlinear influence of urban morphology factors on PM2.5 concentration. In addition, random forest (RF), decision trees (DT), and multiple linear regression (MLR) models were selected to compare the prediction accuracy of the GBRT model. The results show that the GBRT model has the highest accuracy, with R2 reaching 0.981; building density (57%) and average building height (49%) were the two most significant factors affecting PM2.5 concentration.

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“…The proportion of moderately or severely polluted days (visibility ≥ 5 km; aerosol optical depth (AOD) > 0.5) ranged from 10.77% to 32.79% from 2000 to 2010 [46]. Solid fuel (e.g., biomass fuel combustion) is an important source of BC for winter heating in the north of China [36,47,48]. Although clean heating has only been implemented in the rural areas of Qingdao, most areas have not yet completely adopted clean heating.…”

Section: Introductionmentioning

confidence: 99%

One-Year Real-Time Measurement of Black Carbon in the Rural Area of Qingdao, Northeastern China: Seasonal Variations, Meteorological Effects, and the COVID-19 Case Analysis

et al. 2021

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In this paper, we report the results obtained from one year of real-time measurement (i.e., from December 2019 to November 2020) of atmospheric black carbon (BC) under a rural environment in Qingdao of Northeastern China. The annual average concentration of BC was 1.92 ± 1.89 μg m−3. The highest average concentration of BC was observed in winter (3.65 ± 2.66 μg m−3), followed by fall (1.73 ± 1.33 μg m−3), spring (1.53 ± 1.33 μg m−3), and summer (0.83 ± 0.56 μg m−3). A clear weekend effect was observed in winter, which was characterized by higher BC concentration (4.60 ± 2.86 μg m−3) during the weekend rather than that (3.22 ± 2.45 μg m−3) during weekdays. The influence of meteorological parameters, including surface horizontal wind speed, boundary layer height (BLH), and precipitation, on BC, was investigated. In particular, such BLH influence presented evidently seasonal dependence, while there was no significant seasonality for horizontal wind speed. These may reflect different roles of atmospheric vertical dilution on affecting BC in different seasons. The △BC/△CO ratio decreased with the increase of precipitation, indicative of the influence of below-cloud wet removal of BC, especially during summertime where rainfall events more frequently occurred than any of other seasons. The bivariate-polar-plot analysis showed that the high BC concentrations were mainly associated with low wind speed in all seasons, highlighting an important BC source originated from local emissions. By using concentration-weighted trajectory analysis, it was found that regional transports, especially from northeastern in winter, could not be negligible for contributing to BC pollution in rural Qingdao. In the coronavirus disease 2019 (COVID−19) case analysis, we observed an obvious increase in the BC/NO2 ratio during the COVID-19 lockdown, supporting the significant non-traffic source sector (such as residential coal combustion) for BC in rural Qingdao.

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Spatial distribution and sources of winter black carbon and brown carbon in six Chinese megacities

Cited by 49 publications

References 68 publications

The optical properties and in-situ observational evidence for the formation of brown carbon in cloud

The optical properties and in-situ observational evidence for the formation of brown carbon in cloud

Assessing the Effects of Urban Morphology Parameters on PM2.5 Distribution in Northeast China Based on Gradient Boosted Regression Trees Method

One-Year Real-Time Measurement of Black Carbon in the Rural Area of Qingdao, Northeastern China: Seasonal Variations, Meteorological Effects, and the COVID-19 Case Analysis

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