WRF-Chem modeling of particulate matter in the Yangtze River Delta region: Source apportionment and its sensitivity to emission changes

Li, Nan; Lu, Yilei; Liao, Hong; He, Qingyang; Li, Jingyi; Long, Xinping

doi:10.1371/journal.pone.0208944

Cited by 19 publications

(9 citation statements)

References 62 publications

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“…The monthly BC patterns were slightly different from those in some previous studies in Shanghai, which showed the lowest monthly BC concentration in September (Feng et al, 2014; Wang, He, et al, 2014). The discrepancy of the patterns among different locations in Shanghai is probably affected by the differences in emission sources and the meteorological conditions in different years (Li et al, 2018; Xu et al, 2015). CO, another air pollutant originating from incomplete combustion, showed similar monthly patterns with BC (Figure S3h).…”

Section: Resultsmentioning

confidence: 99%

“…The average BC concentration on the 30 haze days was 4.32 ± 1.37 μg/m 3 (0.25 μg/m 3 ), which was significantly higher than that on nonhaze days (1.99 ± 1.08 μg/m 3 (0.06 μg/m 3 )) (Figure 3). Local anthropogenic emissions and meteorological conditions were the major factors contributing to the formation and evolution of haze events in the YRD area (Li et al, 2018; Xu et al, 2015). The higher BC concentrations on haze days also suggested that BC was coemitted and cotransported with other air pollutants from local and remote sources and accumulated under unfavorable weather conditions (Xu et al, 2015).…”

Section: Resultsmentioning

confidence: 99%

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Temporal Characteristics and Potential Sources of Black Carbon in Megacity Shanghai, China

Wei

Wang

et al. 2020

JGR Atmospheres

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Black carbon (BC) is a major light absorption material that acts as a climate change driver with high radiative forcing and as an air pollutant that reduces visibility and air quality. Thus, reducing the emission and ambient concentration of BC could help address climate change and improve air quality simultaneously. In this study, the mass concentration of atmospheric BC was continuously measured by an aethalometer in Shanghai in 2017. The annual BC concentration was 2.19 ± 1.28 μg/m 3 , with the highest loading in winter and the lowest loading in autumn. The BC concentrations varied with year and location when compared with previous studies in different locations in Shanghai. The hourly BC concentration had a bimodal distribution, with two peaks during the morning and evening traffic rush hours. Liquid fuels, biomass, and coal combustion contributed 65.7%, 21.5%, and 12.8%, respectively, of the total BC based on the advanced aethalometer model. The three sources varied in different seasons with a high contribution of liquid source in summer and more coal and biomass emissions in winter. High BC concentrations accumulated in the stable weather conditions in the four seasons and appeared when there were high wind speeds from the northwestern direction in winter. The Yangtze River Delta region was the most likely potential source region of high BC loading in the four seasons, and long-range transport from North China in winter was another likely source region based on the results of cluster analysis and potential source contribution function analysis of backward trajectories. Key Points:• One-year continuous black carbon (BC) concentration was observed and compared with previous studies in different locations of Shanghai • Hourly BC showed a bimodal distribution, with two peaks during the morning and evening rush hours • Liquid fuel was the domain source of BC, followed by biomass and coal emissions originating from local emissions and long-range transportSupporting Information:• Supporting Information S1

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Temporal Characteristics and Potential Sources of Black Carbon in Megacity Shanghai, China

Wei

Wang

et al. 2020

JGR Atmospheres

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“…During severe haze events, the observed maximum hourly surface-layer PM 2.5 (fine particulate matter with an aerodynamic diameter of 2.5 µm or less) concentration can exceed 1000 µg m −3 (Z. Sun et al, 2016;, which can significantly influence visibility (Li et al, 2014), the radiation budget (Steiner et al, 2013), atmospheric circulation (Jiang et al, 2017), cloud properties (Unger et al, 2009), and human health (Hu et al, 2014;Guo et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Assessing the formation and evolution mechanisms of severe haze pollution in the Beijing–Tianjin–Hebei region using process analysis

et al. 2019

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Abstract. Fine-particle pollution associated with haze threatens human health, especially in the North China Plain region, where extremely high PM2.5 concentrations are frequently observed during winter. In this study, the Weather Research and Forecasting with Chemistry (WRF-Chem) model coupled with an improved integrated process analysis scheme was used to investigate the formation and evolution mechanisms of a haze event over the Beijing–Tianjin–Hebei (BTH) region in December 2015; this included an examination of the contributions of local emissions and regional transport to the PM2.5 concentration in the BTH area, and the contributions of each detailed physical or chemical process to the variations in the PM2.5 concentration. The mechanisms influencing aerosol radiative forcing (including aerosol direct and indirect effects) were also examined by using process analysis. During the aerosol accumulation stage (16–22 December, Stage 1), the near-surface PM2.5 concentration in the BTH region increased from 24.2 to 289.8 µg m−3, with the contributions of regional transport increasing from 12 % to 40 %, while the contribution of local emissions decreased from 59 % to 38 %. During the aerosol dispersion stage (23–27 December, Stage 2), the average concentration of PM2.5 was 107.9 µg m−3, which was contributed by local emissions (51 %) and regional transport (24 %). The 24 h change (23:00 minus 00:00 LST) in the near-surface PM2.5 concentration was +43.9 µg m−3 during Stage 1 and −41.5 µg m−3 during Stage 2. The contributions of aerosol chemistry, advection, and vertical mixing to the 24 h change were +29.6 (+17.9) µg m−3, −71.8 (−103.6) µg m−3, and −177.3 (−221.6) µg m−3 during Stage 1 (Stage 2), respectively. Small differences in the contributions of other processes were found between Stage 1 and Stage 2. Therefore, the PM2.5 increase over the BTH region during the haze formation stage was mainly attributed to strong production by the aerosol chemistry process and weak removal by the advection and vertical mixing processes. When aerosol radiative feedback was considered, the 24 h PM2.5 increase was enhanced by 4.8 µg m−3 during Stage 1, which could be mainly attributed to the contributions of the vertical mixing process (+22.5 µg m−3), the advection process (−19.6 µg m−3), and the aerosol chemistry process (+1.2 µg m−3). The restrained vertical mixing was the primary reason for the enhancement in the near-surface PM2.5 increase when aerosol radiative forcing was considered.

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“…Among these, input emission data used in each study may have been the main reason for the differences in the results of each source apportionment simulation. The zero-out method used in this study is a special case of the brute-force method which has been widely used to estimate the contributions of local and non-local emission sources or sectoral emission sources to the concentration of air pollutants at specific locations [32,[58][59][60]. The BFM quantifies the contribution of one or more emission sources to the concentration of air pollutants at specific locations through a series of simulations including a base case simulation where no changes to emission sources are made and other simulations with modified emissions.…”

Section: Contributions Of Different Emission Sectors Inside Hanoi To ...mentioning

confidence: 99%

Air Quality Modeling Study on the Controlling Factors of Fine Particulate Matter (PM2.5) in Hanoi: A Case Study in December 2010

2020

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Meteorology and emission sources are the two main factors determining concentrations of air pollutants, including fine particulate matter. A regional air quality modeling system was used to analyze the sources of fine-particulate air pollution in Hanoi, Vietnam, in December 2010. The impacts of precipitation and winds on PM2.5 concentrations was investigated. Precipitation was negatively correlated with PM2.5 concentrations. However, winds showed both positive and negative correlations with PM2.5 concentrations, depending on wind direction (WD) and the level of upwind concentrations. Sensitivity simulations were conducted to investigate the contribution of local and non-local emissions sources on total PM2.5 by perturbing the emission inputs of the model. Overall, local and non-local sources contributed equally to the total PM2.5 in Hanoi. Local emission sources comprised 57% of the total PM2.5 concentrations for the high PM2.5 pollution levels, while only comprising 42% of the total PM2.5 for low levels of PM2.5 concentrations. In Hanoi’s urban areas, local sources contributed more to the total PM2.5 than non-local sources. In contrast, non-local sources were the main contributors to the PM2.5 in Hanoi’s rural areas. Additional sensitivity simulations were conducted to identify the main local emission sources of PM2.5 concentrations in December 2010. The industrial and residential sectors collectively comprised 79% of the total PM2.5 concentrations while the transport and power sectors comprised only 2% and 3%, respectively. This is the first case study which used a regional air quality modeling system to provide new and informative insights into PM2.5 air pollution in Hanoi by estimating the contributions of local and non-local emissions sources, as well as the contribution of local emission sectors to PM2.5 concentrations in Hanoi.

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WRF-Chem modeling of particulate matter in the Yangtze River Delta region: Source apportionment and its sensitivity to emission changes

Cited by 19 publications

References 62 publications

Temporal Characteristics and Potential Sources of Black Carbon in Megacity Shanghai, China

Temporal Characteristics and Potential Sources of Black Carbon in Megacity Shanghai, China

Assessing the formation and evolution mechanisms of severe haze pollution in the Beijing–Tianjin–Hebei region using process analysis

Air Quality Modeling Study on the Controlling Factors of Fine Particulate Matter (PM2.5) in Hanoi: A Case Study in December 2010

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