Modeling assessment of air pollution control measures and COVID-19 pandemic on air quality improvements over Greater Bay Area of China.

A remarkable progress has been made toward the air quality improvements over the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China from 2017 to 2020. In this study, for the first time, the emission reductions of regional control measures together with the COVID-19 pandemic were considered simultaneously into the development of the GBA's emission inventories for the years of 2017 and 2020. Based on these collective emission inventories, the impacts of control measures, meteorological variations together with temporary COVID-19 lockdowns on the five major air quality index pollutants (SO2, NO2, PM2.5, PM10, and O3, excluding CO) were evaluated using the WRF-CMAQ and SMAT-CE model attainment assessment tool over the GBA region. Our results revealed that control measures in the Pearl River Delta (PRD) region affected significantly the GBA, resulting in pollutant reductions ranging from 48 % to 64 %. In contrast, control measures in Hong Kong and Macao contributed to pollutant reductions up to 10 %. In PRD emission sectors, stationary combustion, on-road, industrial processes and dust sectors stand out as the primary contributors to overall air quality improvements. Moreover, the COVID-19 pandemic during period I (Jan 23-Feb 23) led to a reduction of NO2 concentration by 7.4 %, resulting in a negative contribution (disbenefit) for O3 with an increase by 2.4 %. Our findings highlight the significance of PRD control measures for the air quality improvements over the GBA, emphasizing the necessity of implementing more refined and feasible manageable joint prevention and control policies.

Development of a recurrent spatiotemporal deep-learning method coupled with data fusion for correction of hourly ozone forecasts.

Ambient ozone (O3) predictions can be very challenging mainly due to the highly nonlinear photochemistry among its precursors, and meteorological conditions and regional transport can further complicate the O3 formation processes. The emission-based chemical transport models (CTM) are broadly used to predict O3 formation, but they may deviate from observations due to input uncertainties such as emissions and meteorological data, in addition to the treatment of O3 nonlinear chemistry. In this study, an innovative recurrent spatiotemporal deep-learning (RSDL) method with model-monitor coupled convolutional recurrent neural networks (ConvRNN) has been developed to improve O3 predictions of CTM. The RSDL method was first used to build the ConvRNN within a 24-h scale to characterize the spatiotemporal relationships between the monitored O3 data and CTM simulations, and then incorporated the recurrent pattern to achieve 72-h multi-site forecasts based on a pilot study over the Pearl River Delta (PRD) region of China. The results showed that the RSDL method predicted O3 with high accuracy over this case study, with an increase of 27.54% in the correlation coefficient (R) average for all sites as well as an increase in R of 0.14-0.21 for all cities compared to CTM. Moreover, the regional distribution of CTM was further improved by the RSDL predictions with the data fusion technique, which greatly reduced the underpredictions of O3 concentrations, particularly in high O3-level areas (concentrations >160 µg/m3), with a 33.55% reduction in the mean absolute error (MAE).

Source and sectoral contribution analysis of PM2.5 based on efficient response surface modeling technique over Pearl River Delta Region of China.

Identifying and quantifying source contributions of pollutant emissions are crucial for an effective control strategy to break through the bottleneck in reducing ambient PM2.5 levels over the Pearl River Delta (PRD) region of China. In this study, an innovative response surface modeling technique with differential method (RSM-DM) has been developed and applied to investigate the PM2.5 contributions from multiple regions, sectors, and pollutants over the PRD region in 2015. The new differential method, with the ability to reproduce the nonlinear response surface of PM2.5 to precursor emissions by dissecting the emission changes into a series of small intervals, has shown to overcome the issue of the traditional brute force method in overestimating the accumulative contribution of precursor emissions to PM2.5. The results of this case study showed that PM2.5 in the PRD region was generally dominated by local emission sources (39-64%). Among the contributions of PM2.5 from various sectors and pollutants, the primary PM2.5 emissions from fugitive dust source contributed most (25-42%) to PM2.5 levels. The contributions of agriculture NH3 emissions (6-13%) could also play a significant role compared to other sectoral precursor emissions. Among the NOX sectors, the emissions control of stationary combustion source could be most effective in reducing PM2.5 levels over the PRD region.

Real-time source contribution analysis of ambient ozone using an enhanced meta-modeling approach over the Pearl River Delta Region of China.

The nonlinear response of O3 to nitrogen oxides (NOx) and volatile organic compounds (VOC) is not conducive to accurately identify the various source contributions and O3-NOx-VOC relationships. An enhanced meta-modeling approach, polynomial functions based response surface modeling coupled with the sectoral linear fitting technique (pf-ERSM-SL), integrating a new differential method (DM), was proposed to break through the limitation. The pf-ERSM-SL with DM was applied for analysis of O3 formation regime and real-time source contributions in July and October 2015 over the Pearl River Delta Region (PRD) of Mainland China. According to evaluations, the pf-ERSM-SL with DM was proven to be effective in source apportionment when the traditional sensitivity analysis was unsuitable for deriving the source contributions in the nonlinear system. After diagnosing the O3-NOx-VOC relationships, O3 formation in most regions of the PRD was identified as a distinctive NOx-limited regime in July; in October, the initial VOC-limited regime was found at small emission reductions (less than 22-44%), but it will transit to NOx-limited when further reductions were implemented. Investigation of the source contributions suggested that NOx emissions were the dominated contributor when turning-off the anthropogenic emissions, occupying 85.41-94.90% and 52.60-75.37% of the peak O3 responses in July and October respectively in the receptor regions of the PRD; NOx emissions from the on-road mobile source (NOx_ORM) in Guangzhou (GZ), Dongguan&Shenzhen (DG&SZ) and Zhongshan (ZS) were identified as the main contributors. Consequently, the reinforced control of NOx_ORM is highly recommended to lower the ambient O3 in the PRD effectively.