New method for evaluating winter air quality: PM2.5 assessment using Community Multi-Scale Air Quality Modeling (CMAQ) in Xi'an

et al. 2021

Atmos. Chem. Phys.

Self Cite

Abstract. Satellite observations reveal that China has been leading the global greening trend in the past 2 decades. We assessed the impact of land cover change as well as climate variability on total biogenic volatile organic compound (BVOC) emission in China from 2001–2016. We found the greening trend in China is leading a national-scale increase in BVOC emission. The BVOC emission level in 2016 could be 11.7 % higher than that in 2001 because of higher tree cover fraction and vegetation biomass. On the regional scale, the BVOC emission level from 2013–2016 could be 8.6 %–19.3 % higher than that from 2001–2004 in hotspots including (1) northeastern China, (2) Beijing and its surrounding areas, (3) the Qin Mountains, (4) Yunnan Province, (5) Guangxi–Guangdong provinces, and (6) Hainan island because of the land cover change without considering the impact of climate variability. The comparison among different scenarios showed that vegetation changes resulting from land cover management are the main driver of BVOC emission change in China. Climate variability contributed significantly to interannual variations but not much to the changing trend during the study period. In the standard scenario, which considers both land cover change and climate variability, a statistically significant increasing trend can still be found in regions including Beijing and its surroundings, Yunnan Province, and Hainan island, and BVOC emission total amount in these regions from 2013–2016 is 11.0 %–17.2 % higher that from 2001–2004. We compared the long-term HCHO vertical columns (VC) from the satellite-based Ozone Monitoring Instrument (OMI) with the estimation of isoprene emission in summer. The results showed statistically significant positive correlation coefficients over the regions with high vegetation cover fractions. In addition, the isoprene emission and HCHO VC both showed statistically significant increasing trends in the south of China where these two variables have high positive correlation coefficients. This result may support our estimation of the variability and trends of BVOC emission in this region; however, the comparison still has large uncertainties since the chemical and physical processes, including transportation, diffusion and chemical reactions, were not considered. Our results suggest that the continued increase in BVOC will enhance the importance of considering BVOC when making policies for controlling ozone pollution in China along with ongoing efforts to increase the forest cover fraction.

Section: Comparison With Other Studies and Discussion Of Uncertaintiesmentioning

confidence: 99%

A long-term estimation of biogenic volatile organic compound (BVOC) emission in China from 2001–2016: the roles of land cover change and climate variability

Wang

et al. 2021

Atmos. Chem. Phys.

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“…daily peak concentration and 8.2 ppb for the 24h concentration in the urban region of Xi'an, one of the biggest cities near the Qinling Mountains suffering poor air quality in recent years (Yang et al, 2019). For southern China, Situ et al (2013) reported that BVOC emission could contribute an average 7.9 ppb surface peak O3 concentration for the urban area in the Pearl River Delta region, and the contribution from BVOC even reached 24.8 ppb over PRD in November.…”

Section: The Regional Variability Of Bvoc Emission In Chinamentioning

confidence: 98%

Land cover change dominates decadal trends of biogenic volatile organic compound (BVOC) emission in China

Wang

et al. 2020

Preprint

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Abstract. Satellite observations reveal that China has been leading the global greening trend in the past two decades. We assessed the impact of land cover change on total BVOC emission in China during 2001–2016 and found a significant increasing trend of 1.09 % yr−1 with increases of 1.35, 1.25 and 1.43 % yr−1 for isoprene, monoterpenes and sesquiterpenes, respectively. Comparison of different scenarios showed that vegetation change is the main driver of BVOC emission change in China. Considerable heterogeneity was observed on regional scales, with the highest increasing trends of BVOC emission found in the Qinling Mountains and in the south of China. The BVOC emission for the year 2016 in these two regions was enhanced by 61.89 and 67.64 % compared to that of 2001, respectively. We compared the long-term HCHO vertical columns (VC) from the satellite-based Ozone Monitoring Instrument (OMI) with the estimation of isoprene emission in summer. The results showed statistically significant positive correlation coefficients over the regions with high vegetation cover fractions. In addition, the isoprene emission and HCHO VC both showed statistically significant increasing trends in the south of China where these two variables have high positive correlation coefficients. This result supports our estimation of the variability and trends of BVOC emission in China. Although anthropogenic sources comprise ∼63 % NMVOC emissions in China, the continued increase of BVOC will enhance the importance of considering BVOC when making policies for controlling ozone pollution in China along with ongoing efforts to reduce anthropogenic emissions.

“…In previous studies, Yang et al (2019) used WRF to drive the CMAQ model for winter air quality in Xi'an, and the model evaluations for winter in 2016 showed that the MB, ME, R, and RMSE of T2 were −2.83 • C, 2.83 • C, 0.89, and 3.29 • C respectively. The MB, ME, R, and RMSE of RH2 were 9.59 %, 10.63 %, 0.71, and 13.43 % respectively.…”

Section: Meteorological Fieldsmentioning

confidence: 99%

“…The statistical results showed that the MB and R of T2 were 2.1 • C and 0.84, and those of RH2 were −15.8 % and 0.65 respectively. Using the same model configuration and monitoring sites, Yang et al (2020) compared the simulated and observed wind speeds at an altitude of 10 m (W10)…”

Section: Meteorological Fieldsmentioning

confidence: 99%

“…Wu et al (2014) improved model performance by adding more regional point source emissions and updating the area source emissions in villages and surrounding cities in Beijing. Based on that work, Yang et al (2019) added local datasets to the emission inventory of the Guanzhong Plain (China), which was applied to simulate fine particulate matter (PM 2.5 ) concentrations using the CMAQ model in Xi'an. Numerous studies have indicated that construction dust emissions play an important role in air pollution, especially in urban areas (Ni et al, 2012;Huang et al, 2014;Wang et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Numerical study of the effects of initial conditions and emissions on PM<sub>2.5</sub> concentration simulations with CAMx v6.1: a Xi'an case study

Xiao

et al. 2021

Geosci. Model Dev.

Self Cite

Abstract. A series of model sensitivity experiments was designed to explore the effects of different initial conditions and emissions in Xi'an in December 2016; Xi'an is a major city in the Fenwei Plain, which is a key area with respect to air pollution control in China. Three methods were applied for the initial condition tests: a clean initial simulation, a restart simulation, and a continuous simulation. In the clean initial simulation test, the C00, C06, C12, C18, and C24 sensitivity experiments were conducted to explore the effect of the intercepted time periods used. The results of these experiments showed that the fine particulate matter (PM2.5) model performance was better when the start time of the intercepted time periods was delayed. For experiments C00 to C24, the absolute mean bias (MB) decreased from 51.07 to 3.72 µg m−3, and the index of agreement (IOA) increased from 0.49 to 0.86, which illustrates that the model performance of C24 is much better than that of C00. The R1120 and R1124 sensitivity experiments were used to explore the restart simulation and, in turn, the effect of the date of the first day of the model simulation. While the start times of the simulations were different, the simulation results with different start times were nearly consistent after a spin-up time period, and the results revealed that the spin-up time was approximately 27 h. For the continuous simulation test, the CT12 and CT24 sensitivity experiments were conducted. The start times of the intercepted time periods for CT12 and R1120 were the same, and the simulation results were almost identical. Based on the simulation results, CT24 showed the best performance of all of the sensitivity experiments, with the correlation coefficient (R), MB, and IOA reaching 0.81, 6.29 µg m−3, and 0.90 respectively. For the emission tests, an updated local emission inventory with construction fugitive dust emissions was added and was compared with the simulation results from the original emission inventory. The simulation with the updated local emissions showed much better performance for PM2.5 modelling. Therefore, combining the CT24 method and the updated local emission inventory can satisfactorily improve the PM2.5 model performance in Xi'an: the absolute MB decreased from 35.16 to 6.29 µg m−3, and the IOA reached 0.90.