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).

Impact of commercial cooking on urban PM2.5 and O3 with online data-assisted emission inventory.

Commercial cooking (CC) is an intensive near-field source contributing to ambient PM2.5 and O3 concentration in urban areas. Compilation of CC emission inventory has been challenging due to the dynamic variation of the emission sector, which has resulted in data deficiencies including underestimated quantity and poor temporal-spatial resolution. In this study, we have developed a methodology that integrates existing emission statistics with online oil fumes monitoring (OOFM) data to create a highly spatiotemporally resolved emission inventory of CC. The new emission estimate differs from legacy inventory in emission quantity and temporal pattern. Using the emission data, the impacts of CC emission on local PM2.5 and O3 were evaluated using WRF-CMAQ and model-monitor data fusion tool of SMAT-CE in Shunde, China. The OOFM data-assisted emission inventory led to improved model performance for both model-predicted PM2.5 and O3 concentrations. The simulation results using the new inventory data showed that the CC emissions contributed 1.25±2 µg/m3 of PM2.5, and accounted for 24±1 % of PM2.5 concentration derived from local anthropogenic emissions. Moreover, a higher contribution of CC to PM2.5 was predicted in areas with elevated CC emissions, while the contribution to O3 was insignificant.

Antioxidant Activities of Aqueous Extracts and Protein Hydrolysates from Marine Worm Hechong (Tylorrhynchus heterochaeta).

Hechong (Tylorrhynchus heterochaeta) is an edible marine worm widely distributed in the estuary area. The objective of this study is to determine the antioxidant activities of extracts and protein hydrolysates from Hechong. Results showed that the aqueous extracts of steamed Hechong had the highest antioxidant values using the methods of DPPH, ABTS, and FRAP testing (76.29 µmol TE/g, 181.04 µmol TE/g, and 10.40 mmol Fe2+/100 g, respectively). Furthermore, protein hydrolysates of Hechong were observed significant antioxidant activities when compared to crude Hechong. The purification was carried out by DEAE-52 cellulose and Sephadex G-100 column chromatography. The microspatial structure of glycoprotein showed fibrous shapes and cracks with uniform distribution. The study has concluded that the extract and protein hydrolysates of Hechong have significant antioxidant activities, which is merited to be further investigated in the food and pharmaceutical fields.

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.