VOC emission caps constrained by air quality targets based on response surface model: A case study in the Pearl River Delta Region, China.

Because of the recent growth in ground-level ozone and increased emission of volatile organic compounds (VOCs), VOC emission control has become a major concern in China. In response, emission caps to control VOC have been stipulated in recent policies, but few of them were constrained by the co-control target of PM2.5 and ozone, and discussed the factor that influence the emission cap formulation. Herein, we proposed a framework for quantification of VOC emission caps constrained by targets for PM2.5 and ozone via a new response surface modeling (RSM) technique, achieving 50% computational cost savings of the quantification. In the Pearl River Delta (PRD) region, the VOC emission caps constrained by air quality targets varied greatly with the NOx emission reduction level. If control measures in the surrounding areas of the PRD region were not considered, there could be two feasible strategies for VOC emission caps to meet air quality targets (160 µg/m3 for the maximum 8-hr-average 90th-percentile (MDA8-90%) ozone and 25 µg/m3 for the annual average of PM2.5): a moderate VOC emission cap with <20% NOx emission reductions or a notable VOC emission cap with >60% NOx emission reductions. If the ozone concentration target were reduced to 155 µg/m3, deep NOx emission reductions is the only feasible ozone control measure in PRD. Optimization of seasonal VOC emission caps based on the Monte Carlo simulation could allow us to gain higher ozone benefits or greater VOC emission reductions. If VOC emissions were further reduced in autumn, MDA8-90% ozone could be lowered by 0.3-1.5 µg/m3, equaling the ozone benefits of 10% VOC emission reduction measures. The method for VOC emission cap quantification and optimization proposed in this study could provide scientific guidance for coordinated control of regional PM2.5 and O3 pollution in China.

A meteorologically adjusted ensemble Kalman filter approach for inversing daily emissions: A case study in the Pearl River Delta, China.

The conventional Ensemble Kalman filter (EnKF), which is now widely used to calibrate emission inventories and to improve air quality simulations, is susceptible to simulation errors of meteorological inputs, making accurate updates of high temporal-resolution emission inventories challenging. In this study, we developed a novel meteorologically adjusted inversion method (MAEInv) based on the EnKF to improve daily emission estimations. The new method combines sensitivity analysis and bias correction to alleviate the inversion biases caused by errors of meteorological inputs. For demonstration, we used the MAEInv to inverse daily carbon monoxide (CO) emissions in the Pearl River Delta (PRD) region, China. In the case study, 60% of the total CO simulation biases were associated with sensitive meteorological inputs, which would lead to the overestimation of daily variations of posterior emissions. Using the new inversion method, daily variations of emissions shrank dramatically, with the percentage change decreased by 30%. Also, the total amount of posterior CO emissions estimated by the MAEInv decreased by 14%, indicating that posterior CO emissions might be overestimated using the conventional EnKF. Model evaluations using independent observations revealed that daily CO emissions estimated by MAEInv better reproduce the magnitude and temporal patterns of ambient CO concentration, with a higher correlation coefficient (R, +37.0%) and lower normalized mean bias (NMB, -17.9%). Since errors of meteorological inputs are major sources of simulation biases for both low-reactive and reactive pollutants, the MAEInv is also applicable to improve the daily emission inversions of reactive pollutants.

Insight into the characteristics of carbonaceous aerosols at urban and regional sites in the downwind area of Pearl River Delta region, China.

Carbonaceous aerosols (CAs) take up a substantial fraction of fine particle (PM2.5) in the atmosphere, yet high temporal resolution and seasonal variations of their emission sources and formation mechanisms are still poorly characterized in the regions with strong anthropogenic activities. In this study, the spatiotemporal characteristics of CAs and their subfractions, i.e., organic carbon (OC) and elemental carbon (EC), were studied in one of China's key city clusters, the Pearl River Delta (PRD) region. Results show that the annual mean OC and EC concentrations are 5.89 ± 3.32 µg/m3 and 1.60 ± 1.00 µg/m3 at the urban site, respectively. Such levels are consistently higher than those at the regional site (4.94 ± 3.34 µg/m3 of OC and 1.45 ± 0.82 µg/m3 of EC), suggesting the strong impact of human activities on OC and EC concentration. Moreover, the OC concentration peak sharply appears at 19:00 across all seasons at the urban site due to the direct influence of traffic exhaust and cooking activities. At regional site, OC peaks in fall afternoon due to intensive photochemical reactions derived combustion-related secondary organic carbon (SOCcom) contributions to the downwind PRD region. Correlations between SOCcom and influence factors were found at both regional and urban sites, suggesting that SOCcom formation is more regionally homogenous and mainly originates from the Zhaoqing-Foshan-Jiangmen belt. In addition, there are significantly different formation mechanisms of non-combustion-related secondary organic carbon (SOCnon-com) in the downwind PRD region. This study provides a solid evidence for collaborative efforts in the mitigation of secondary aerosols in the PRD region.

An updated model-ready emission inventory for Guangdong Province by incorporating big data and mapping onto multiple chemical mechanisms.

An accurate characterization of spatial-temporal emission patterns and speciation of volatile organic compounds (VOCs) for multiple chemical mechanisms is important to improving the air quality ensemble modeling. In this study, we developed a 2017-based high-resolution (3 km × 3 km) model-ready emission inventory for Guangdong Province (GD) by updating estimation methods, emission factors, activity data, and allocation profiles. In particular, a full-localized speciation profile dataset mapped to five chemical mechanisms was developed to promote the determination of VOC speciation, and two dynamic approaches based on big data were used to improve the estimation of ship emissions and open fire biomass burning (OFBB). Compared with previous emissions, more VOC emissions were classified as oxygenated volatile organic compound (OVOC) species, and their contributions to the total ozone formation potential (OFP) in the Pearl River Delta (PRD) region increased by 17%. Formaldehyde became the largest OFP species in GD, accounting for 11.6% of the total OFP, indicating that the model-ready emission inventory developed in this study is more reactive. The high spatial-temporal variability of ship sources and OFBB, which were previously underestimated, was also captured by using big data. Ship emissions during typhoon days and holidays decreased by 23-55%. 95% of OFBB emissions were concentrated in 9% of the GD area and 31% of the days in 2017, demonstrating their strong spatial-temporal variability. In addition, this study revealed that GD emissions have changed rapidly in recent years due to the leap-forward control measures implemented, and thus, they needed to be updated regularly. All of these updates led to a 5-17% decrease in the emission uncertainty for most pollutants. The results of this study provide a reference for how to reduce uncertainties in developing model-ready emission inventories.