Recent developments of anthropogenic air pollutant emission inventories in Guangdong province, China

Zhong, Zhuangmin; Zheng, Junyu; Zhu, Manni; Huang, Zhijiong; Zhang, Zhiwei; Jia, Guanglin; Wang, Xiaoli; Bian, Yahui; Wang, Yanlong; Li, Nan

doi:10.1016/j.scitotenv.2018.01.268

Cited by 101 publications

(42 citation statements)

References 35 publications

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“…Additionally, TC was significantly influenced by the regional transport on these days. Specifically, it was expected that SO 2 would be higher at QAI due to the ship emissions and the higher background levels in inland PRD (Li, Yuan, et al, ; Zhong et al, ), which was also confirmed in Patterns 2 and 3 (Table S3). However, in Pattern 1, SO 2 at TC (6.0 ± 1.0 ppbv) significantly surpassed that at QAI (3.9 ± 0.8 ppbv).…”

Section: Resultsmentioning

confidence: 72%

An Ozone “Pool” in South China: Investigations on Atmospheric Dynamics and Photochemical Processes Over the Pearl River Estuary

et al. 2019

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Pearl River Estuary (PRE) has been recognized as one of the key areas in South China experiencing severe air pollution. To understand the photochemical pollution over PRE, intensive measurements of O 3 and its precursors were simultaneously conducted at a suburban site on the east bank of PRE (Tung Chung, TC) in Hong Kong and a rural site on the west bank (Qi'ao Island, QAI) in Zhuhai from 25 September to 29 November 2016. Three and sixteen O 3 episode days (days with the maximum hourly O 3 higher than 100 ppbv) were captured at TC and QAI, respectively. On the three days when O 3 episodes occurred at TC, QAI also suffered from O 3 episodes, which were all associated with tropical cyclones (typhoons). As a downwind site of PRE when northwesterly winds prevailed, TC displayed a higher maximum hourly O 3 than QAI on these three days. In contrast, the other thirteen episodes that solely occurred at QAI were generally under northeasterly winds, which brought more coastal air to TC and led to lower levels of O 3 at this site. In addition, the stronger titration of the locally emitted NO to O 3 also accounted for the lower O 3 at TC on these days. The simulations by Weather Research and Forecasting coupled with Chemistry model indicated that the interactions of synoptic winds and mesoscale breezes led to relatively weak winds and long residence time for air pollutants over the PRE, thereby air pollution accumulation. This provided the likelihood of intensive chemical productions of O 3 over PRE, with the production rates even higher than those in the surrounding land areas. As such, it was concluded that PRE was a "pool" of O 3 , where atmospheric dynamics caused the accumulation of air pollutants and subsequently intensive photochemical reactions. The findings in this study may also apply to the other estuaries connecting terrestrial and marine systems over the world.

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Section: Resultsmentioning

confidence: 72%

An Ozone “Pool” in South China: Investigations on Atmospheric Dynamics and Photochemical Processes Over the Pearl River Estuary

et al. 2019

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“…As shown in Table 4, the uncertainty in the total S-IVOC emissions was very high with a relative error of −79 %-229 % at the 95 % confidence interval, which could be mainly attributed to uncertainties in the S-IVOC emissions of the onroad mobile sources because of the largest correlation coefficient of the on-road mobile S-IVOC emissions with total S-IVOC emissions among all the source categories. It is noteworthy that the uncertainty ranges of the emission inventories of S-IVOCs were wider than those of VOCs and PM 2.5 , which were only −6 %-99 % and −6 %-77 %, respectively (Zhong et al, 2018). For input parameters in the emission model, the correlation coefficients between total S-IVOC emissions and F OC for the on-road mobile sources or ratios of E IVOCs / E POA for all source categories, except biomass burning, were very large, indicating that these parameters were the key sources of high uncertainties in the S-IVOC emission estimates.…”

Section: Uncertainties In S-ivoc Emissionsmentioning

confidence: 92%

“…Based on the different values of model input parameters from previous studies (Table 1), probabilistic distributions representing uncertainty ranges of different parameters, including F OC , E SVOCs/E POA , E IVOCs/E POA , OM/OC, O/C, H/C, and N/C, from different source categories are summarized in Table 2. Additionally, uniform distribution based on the results of uncertainty assessment in Zhong et al (2018) was applied to all source categories of PM 2.5 emission in the present study.…”

Section: Establishment Of the S-ivoc Emission Inventorymentioning

confidence: 99%

“…As the key component, secondary organic aerosol (SOA) accounts for 20 %-80 % of organic aerosol (OA), while OA accounts for 20 %-90 % of fine particulate matter (PM 2.5 ) (Kanakidou et al, 2005;Carlton et al, 2009;Zhang et al, 2007Zhang et al, , 2013. They not only affect atmospheric chemistry, climate change, radiation balance, visibility, and air quality (Kanakidou et al, 2005;Pope et al, 2002), but also endanger human and vegetation health (Gehring et al, 2013;Zhou et al, 2014). In recent years, although PM 2.5 concentrations in major city clusters including the Pearl River Delta (PRD) region have shown a declining trend, the annual PM 2.5 concentrations are still higher than the World Health Organization (WHO) air quality standards and Air Quality Guideline Lin et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Emission inventory of semi-volatile and intermediate-volatility organic compounds and their effects on secondary organic aerosol over the Pearl River Delta region

Wang

Lü

et al. 2019

Atmos. Chem. Phys.

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Abstract. Semi-volatile and intermediate-volatility organic compounds (S–IVOCs) are considered critical precursors of secondary organic aerosol (SOA), which is an important component of fine particulate matter (PM2.5). However, knowledge of the contributions of S–IVOCs to SOA is still lacking in the Pearl River Delta (PRD) region, southern China. Therefore, in this study, an emission inventory of S–IVOCs in the PRD region was developed for the first time for the year 2010. The S–IVOC emissions were calculated based on a parameterization method involving the emission factors of POA (primary organic aerosol), emission ratios of S–IVOCs to POA, and domestic activity data. The total emissions of S–IVOCs were estimated to be 323.4 Gg, with major emissions from central cities in the PRD, i.e., Guangzhou, Foshan, and Shenzhen. On-road mobile sources and industries were the two major contributors of S–IVOC emissions, with contributions of ∼42 % and ∼35 %, respectively. Furthermore, uncertainties of the emission inventory were evaluated through Monte Carlo simulation. The uncertainties ranged from −79 % to 229 %, which could be mainly attributed to mass fractions of OC (organic carbon) to PM2.5 from on-road mobile emissions and emission ratios of IVOCs ∕ POA. The developed S–IVOC emission inventory was further incorporated into the Weather Research and Forecasting with Chemistry (WRF-Chem) model with a volatility basis-set (VBS) approach to improve the performance of SOA simulation and to evaluate the influence of S–IVOCs on SOA formation at a receptor site (Wan Qing Sha (WQS) site) in the PRD. The following results could be obtained. (1) The model could resolve about 34 % on average of observed SOA concentrations at WQS after considering the emissions of S–IVOCs, and 18 %–77 % with the uncertainties of the S–IVOC emission inventory considered. (2) The simulated SOA over the PRD region was increased by 161 % with the input of S–IVOC emissions, and it could be decreased to 126 % after the reaction coefficient of S–IVOCs with OH radical was improved. (3) Among all anthropogenic sources of S–IVOCs, industrial emission was the most significant contributor of S–IVOCs for SOA formation, followed by on-road mobile, dust, biomass burning, residential, and off-road mobile emissions. Overall, this study firstly quantified emissions of S–IVOCs and evaluated their roles in SOA formation over the PRD, which contributes towards significantly improving SOA simulation and better understanding of SOA formation mechanisms in the PRD and other regions in China.

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“…of the total uncertainty in simulated PM 2.5 concentrations ( Figure 6). The primary PM 2.5 emissions generally have high uncertainty in China due to the limited measurements of local emission factors and a dearth of detailed activity data, particularly for fugitive dust, one of the largest contributors to primary PM 2.5 emissions 38 . The key uncertainty sources identified in this case study have previously been uncovered by Huang et al 39 .…”

Section: Uncertainty Attributions Of Pm25mentioning

confidence: 99%

A Feasible Methodological Framework for Uncertainty Analysis and Diagnosis of Atmospheric Chemical Transport Models

Huang

Zheng

et al. 2019

Environ. Sci. Technol.

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The current state of quantifying uncertainty in chemical transport models (CTM) is often limited and insufficient due to numerous uncertainty sources and inefficient or inaccurate uncertainty propagation methods. In this study, we proposed a feasible methodological framework for CTM uncertainty analysis, featuring sensitivity analysis to filter important model inputs and a new reduced-form model (RFM) that couples the High-order Decoupled Direct Method (HDDM) and the Stochastic Response Surface Model (SRSM) to boost uncertainty propagation. Compared with the SRSM, the new RFM approach is 64% more computationally efficient while maintaining high accuracy. The framework was applied to PM 2.5 simulations in the Pearl River Delta (PRD) region, and identified five precursor emissions, two species in lateral boundary conditions (LBCs) and three meteorological inputs out of 203 model inputs as important model inputs based on sensitivity analysis. Among these selected inputs, primary PM 2.5 emissions, PM 2.5 concentrations of LBCs and wind speed were key uncertainty sources, which collectively contributed 81.4% to the total uncertainty in PM 2.5 simulations. Also, when evaluated against observations, we found that there were systematic underestimates in PM 2.5 simulations, which can be attributed to the twoproduct method that describes the formation of secondary organic aerosol.

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Recent developments of anthropogenic air pollutant emission inventories in Guangdong province, China

Cited by 101 publications

References 35 publications

An Ozone “Pool” in South China: Investigations on Atmospheric Dynamics and Photochemical Processes Over the Pearl River Estuary

An Ozone “Pool” in South China: Investigations on Atmospheric Dynamics and Photochemical Processes Over the Pearl River Estuary

Emission inventory of semi-volatile and intermediate-volatility organic compounds and their effects on secondary organic aerosol over the Pearl River Delta region

A Feasible Methodological Framework for Uncertainty Analysis and Diagnosis of Atmospheric Chemical Transport Models

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