Co-benefits of peaking carbon dioxide emissions on air quality and health, a case of Guangzhou, China.

Cities play a key role in making carbon emission reduction targets achievable and tackling air pollution. Using Guangzhou city as a case, this paper explored the air quality and health co-benefits of peaking carbon dioxide emissions under three scenarios and developed an integrated assessment framework by combining a local air pollutant emission inventory, an atmospheric chemistry transport model, and a health assessment model. The results showed that SO2, PM10, and PM2.5 could achieve larger emission reductions than NH3, VOCs, and NOx among all the scenarios we examined. Under the enhanced peaking scenario with the most stringent mitigation strategies, Guangzhou could meet the local ambient air quality standard for PM2.5 (34 µg/m3), with the most reduction observed in the annual average PM2.5 concentration (28.4%) and related premature deaths (17.08%), compared with the base year 2015. We also identified hotspot grids, which were areas with high concentrations of carbon emissions, high concentrations of air pollution and poor air quality in Guangzhou. Our analysis highlighted the importance of promoting peaking carbon dioxide emission for the improvement of air quality and public health at the city level.

NO x Emission Changes Over China During the COVID-19 Epidemic Inferred From Surface NO2 Observations.

The COVID-19 epidemic has substantially limited human activities and affected anthropogenic emissions. In this work, daily NO x emissions are inferred using a regional data assimilation system and hourly surface NO2 measurement over China. The results show that because of the coronavirus outbreak, NO x emissions across the whole mainland China dropped sharply after 31 January, began to rise slightly in certain areas after 10 February, and gradually recover across the country after 20 February. Compared with the emissions before the outbreak, NO x emissions fell by more than 60% and ~30% in many large cities and most small to medium cities, respectively. Overall, NO x emissions were reduced by 36% over China, which were mainly contributed by transportation. Evaluations show that the inverted changes over eastern China are credible, whereas those in western China might be underestimated. These findings are of great significance for exploring the reduction potential of NO x emissions in China.

Effects of air pollution control measures on air quality improvement in Guangzhou, China.

The ambient air quality of Guangzhou in 2016 has significantly improved since Guangzhou and its surrounding cities implemented a series of air pollution control measures from 2014 to 2016. This study not only estimated the effects of meteorology and emission control measures on air quality improvement in Guangzhou but also assessed the contributions of emissions reduction from various sources through the combination of observation data and simulation results from Weather Research and Forecasting - Community Multiscale Air Quality (WRF-CMAQ) modeling system. Results showed that the favorable meteorological conditions in 2016 alleviated the air pollution. Compared to change in meteorology, implementing emission control measures in Guangzhou and surrounding cities was more beneficial for air quality improvement, and it could reduce the concentrations of SO2, NO2, PM2.5, PM10, and O3 by 9.7 µg m-3 (48.4%), 9.2 µg m-3 (17.7%), 7.7 µg m-3 (14.6%), 9.7 µg m-3 (13.4%), and 12.0 µg m-3 (7.7%), respectively. Furthermore, emission control measures that implemented in Guangzhou contributed most to the concentration reduction of SO2, NO2, PM2.5, and PM10 (46.0% for SO2, 15.2% for NO2, 9.4% for PM2.5, and 9.1% for PM10), and it increased O3 concentration by 2.4%. With respect to the individual contributions of source emissions reduction, power sector emissions reduction showed the greatest contribution in reducing the concentrations of SO2, NO2, PM2.5, and PM10 due to the implementation of Ultra-Clean control technology. As for O3 mitigation, VOCs product-related source emissions reduction was most effective, and followed by transportation source emissions reduction, while the reductions of power sector, industrial boiler, and industrial process source might not be as effective. Our findings provide scientific advice for the Guangzhou government to formulate air pollution prevention and control policies in the future.

Improved estimation of CO2 emissions from thermal power plants based on OCO-2 XCO2 retrieval using inline plume simulation.

CO2 emissions from power plants are the dominant source of global CO2 emissions, thus in the context of global warming, accurate estimation of CO2 emissions from power plants is essential for the effective control of carbon emissions. Based on the XCO2 retrievals from the Orbiting Carbon Observatory 2 (OCO-2) and the Gaussian Plume Model (GPM), a series of studies have been carried out to estimate CO2 emission from power plants. However, the GPM is an ideal model, and there are a number of assumptions that need to be made when using this model, resulting in large uncertainties in the inverted emissions. Here, based on 6 cases of power plant plumes observed by the OCO-2 satellite over the Yangtze River Delta, China, we use an inline plume rise module coupled in the Community Multi-scale Air Quality model (CMAQ) to simulate the plumes and invert the emissions, and compare the simulated plumes and inverted emissions using the GPM model. We found that CO2 emissions can be significantly overestimated or underestimated based on the GPM simulations, and that the CMAQ inline plume simulation could significantly improve the estimates. However, the simulation bias in wind speed can significantly affect the inversion results. These results indicate that accurate meteorological field and plume simulations are critical for future inversion of point source emissions.

[Impacts of Anthropogenic Emission Reduction on Urban Atmospheric Oxidizing Capacity During the COVID-19 Lockdown].

In recent years， regional compound air pollution events caused by fine particles （PM2.5） and ozone （O3） have occurred frequently in economically developed areas of China， in which atmospheric oxidizing capacity （AOC） has played an important role. In this study， the WRF-CMAQ model was used to study the impacts of anthropogenic emission reduction on AOC during the COVID-19 lockdown period. Three representative cities in eastern China （Shijiazhuang， Nanjing， and Guangzhou） were selected for an in-depth analysis to quantify the contribution of meteorology and emissions to the changes in AOC and oxidants and to discuss the impact of AOC changes on the formation of secondary pollutants. The results showed that， compared with that in the same period in 2019， the urban average AOC in Shijiazhuang， Nanjing， and Guangzhou in 2020 increased by 60%， 48.7%， and 12.6%， respectively. The concentrations of O3， hydroxyl radical （·OH）， and nitrogen trioxide （NO3·ï¼‰ increased by 1.6%-26.4%， 14.8%-73.3%， and 37.9%-180%， respectively. The AOC in the three cities increased by 0.06×10-4， 0.12×10-4， and 0.33×10-4 min-1， respectively， due to emission reduction. The meteorological change increased AOC in Shijiazhuang and Nanjing by 20% and 17.9%， respectively， but decreased AOC in Guangzhou by -9.3%. Enhanced AOC led to an increase in the nitrogen oxidation ratio （NOR） and VOCs oxidation ratio （VOR） and promoted the transformation of primary pollutants to secondary pollutants. This offset the effects of primary emission reduction and resulted in a nonlinear decline in secondary pollutants compared to emissions during the COVID-19 lockdown.

Quantifying the air quality impact of ship emissions in China's Bohai Bay.

Bohai Bay, as a significant economic bay area in China, has experienced considerable ecological consequences during its rapid economic development. One of the major environmental challenges is the emission of air pollutants from ships, which has had a severe impact on regional air quality and the health of residents. To assess the influence of pollutants on the air quality around the Bohai Bay area, a Weather Research and Forecasting and Community Multiscale Air Quality (WRF-CMAQ) model was established using a 9 km × 9 km high-resolution ship emission gridded inventory from 2018. The WRF-CMAQ model was employed to compare two scenarios: vessel emissions and non-vessel emissions, in order to evaluate the impact of ship emissions. By analyzing the pollutant concentrations in Bohai Bay and the degree of change in pollutant concentration in six cities under these two scenarios, significant differences were observed. Furthermore, a comparison of the hourly concentration contributions of ship emissions between port cities and inland cities within the same region revealed that inland cities were less affected by ship emissions. The main contributing factors to this disparity were identified as wind direction and wind speed.

Anthropogenic emissions estimated using surface observations and their impacts on PM2.5 source apportionment over the Yangtze River Delta, China.

Source-tagged source apportionment (SA) has advantages for quantifying the contribution of various source regions and categories to PM2.5; however, it is highly affected by uncertainty in the emission inventory. In this study, we used a Regional multi-Air Pollutant Assimilation System (RAPAS) to optimize daily SO2, NOx and primary PM2.5 (PPM2.5) emissions in the Yangtze River Delta (YRD) in December 2016 by assimilating hourly in-situ measurements. The CMAQ-ISAM model was implemented with prior and posterior emissions respectively to investigate the impacts of optimizing emissions on PM2.5 SA in the YRD megalopolis (YRDM) and three megacities of Shanghai, Nanjing, and Hangzhou in the YRDM. The results showed that RAPAS significantly improved the simulations and reduced the emission uncertainties of the different pollutants. Compared with prior emissions, the posterior emissions in the YRD decreased by 13% and 11% for SO2 and NOx respectively, and increased by 24% for PPM2.5. Compared with SA using prior emissions, the contributions from Hangzhou, northern Zhejiang, and areas outside of the YRD to the YRDM increased. The local contributions from the YRDM, Nanjing and Shanghai decreased by 1.8%, 9.7%, and 2.3%, respectively, whereas that of Hangzhou increased by 5.6%. The changes in the daily local contributions caused by optimizing emissions ranged from -18.0% to 23.6%. Generally, under stable weather conditions, the local contribution changed the most, whereas under unstable weather conditions, the contribution from upwind areas changed significantly. Overall, with optimized emissions, we found in Nanjing, Shanghai, and Hangzhou, local emissions contributed 18.2%, 39.6% and 36.8% of their PM2.5 concentrations, respectively; long-range transport from outside the YRDM contributed 59.2%, 48.1%, and 48.2%, respectively. This study emphasizes the importance of improving emission estimations for source-tagged SA and provides more reliable SA results for the main cities in the YRD, which will contribute to pollution control in these regions.

Pronounced increases in nitrogen emissions and deposition due to the historic 2020 wildfires in the western U.S.

Wildfire outbreaks can lead to extreme biomass burning (BB) emissions of both oxidized (e.g., nitrogen oxides; NOx = NO+NO2) and reduced form (e.g., ammonia; NH3) nitrogen (N) compounds. High N emissions are major concerns for air quality, atmospheric deposition, and consequential human and ecosystem health impacts. In this study, we use both satellite-based observations and modeling results to quantify the contribution of BB to the total emissions, and approximate the impact on total N deposition in the western U.S. Our results show that during the 2020 wildfire season of August-October, BB contributes significantly to the total emissions, with a satellite-derived fraction of NH3 to the total reactive N emissions (median ~ 40%) in the range of aircraft observations. During the peak of the western August Complex Fires in September, BB contributed to ~55% (for the contiguous U.S.) and ~ 83% (for the western U.S.) of the monthly total NOx and NH3 emissions. Overall, there is good model performance of the George Mason University-Wildfire Forecasting System (GMU-WFS) used in this work. The extreme BB emissions lead to significant contributions to the total N deposition for different ecosystems in California, with an average August - October 2020 relative increase of ~78% (from 7.1 to 12.6 kg ha-1 year-1) in deposition rate to major vegetation types (mixed forests + grasslands/shrublands/savanna) compared to the GMU-WFS simulations without BB emissions. For mixed forest types only, the average N deposition rate increases (from 6.2 to 16.9 kg ha-1 year-1) are even larger at ~173%. Such large N deposition due to extreme BB emissions are much (~6-12 times) larger than low-end critical load thresholds for major vegetation types (e.g., forests at 1.5-3 kg ha-1 year-1), and thus may result in adverse N deposition effects across larger areas of lichen communities found in California's mixed conifer forests.

Contributions of Traffic and Industrial Emission Reductions to the Air Quality Improvement after the Lockdown of Wuhan and Neighboring Cities Due to COVID-19.

Wuhan was locked down from 23 January to 8 April 2020 to prevent the spread of the novel coronavirus disease 2019 (COVID-19). Both public and private transportation in Wuhan and its neighboring cities in Hubei Province were suspended or restricted, and the manufacturing industry was partially shut down. This study collected and investigated ground monitoring data to prove that the lockdowns of the cities had significant influences on the air quality in Wuhan. The WRF-CMAQ (Weather Research and Forecasting-Community Multiscale Air Quality) model was used to evaluate the emission reduction from transportation and industry sectors and associated air quality impact. The results indicate that the reduction in traffic emission was nearly 100% immediately after the lockdown between 23 January and 8 February and that the industrial emission tended to decrease by about 50% during the same period. The industrial emission further deceased after 9 February. Emission reduction from transportation and that from industry was not simultaneous. The results imply that the shutdown of industry contributed significantly more to the pollutant reduction than the restricted transportation.

[Impacts of Meteorology and Emission Variations on PM2.5 Concentration Throughout the Country During the 2020 Epidemic Period].

This study analyzed the impacts of meteorological conditions and changes in air pollutant emissions on PM2.5 across the country during the first quarter of 2020 based on the WRF-CMAQ model. Results showed that the variations in meteorological conditions led to a national PM2.5 concentration decreased of 1.7% from 2020-01 to 2020-03, whereas it increased by 1.6% in January and decreased by 1.3% and 7.9% in February and March, respectively. The reduction of pollutants emissions led to a decrease of 14.1% in national PM2.5 concentration during the first quarter of 2020 and a decrease of 4.0%, 25.7%, and 15.0% in January, February, and March, respectively. Compared to the same period last year, the PM2.5 concentration measured in Wuhan City decreased more than in the entire country. This was caused by improved meteorological conditions and a higher reduction of pollutant emissions in Wuhan City. PM2.5 in Beijing increased annually before the epidemic outbreak and during the strict control period, mainly due to unfavorable meteorological conditions. However, the decrease in PM2.5 in Beijing compared to March 2019 was closely related to the substantial reduction of emissions. The measured PM2.5 in the "2+26" cities, the Fenwei Plain and the Yangtze River Delta (YRD) decreased during the first quarter of 2020, with the largest drop occurring in the Yangtze River Delta due to higher YRD emissions reductions. The meteorological conditions of "2+26" cities and Fenwei Plain were unfavorable before the epidemic outbreak and greatly improved during the strict control period, whereas the Yangtze River Delta had the most favorable meteorological conditions in March. The decrease in PM2.5 concentration caused by the reduction of pollutant emissions in the three key areas was highest during the strict control period.

Impacts of meteorology and emission variations on the heavy air pollution episode in North China around the 2020 Spring Festival.

Based on the Weather Research and Forecasting model and the Models-3 community multi-scale air quality model (WRF-CMAQ), this study analyzes the impacts of meteorological conditions and changes in air pollutant emissions on the heavy air pollution episode occurred over North China around the 2020 Spring Festival (January to Februray 2020). Regional reductions in air pollutant emissions required to eliminate the PM2.5 heavy pollution episode are also quantified. Our results found that meteorological conditions for the Beijing-Tianjin-Hebei and surrounding "2+26" cities are the worst during the heavy pollution episode around the 2020 Spring Festival as compared with two other typical heavy pollution episodes that occurred after 2015. However, because of the substantial reductions in air pollutant emissions in the "2+26" cities in recent years, and the 32% extra reduction in emissions during January to February 2020 compared with the baseline emission levels of the autumn and winter of 2019 to 2020, the maximum PM2.5 level during this heavy pollution episode around the 2020 Spring Festival was much lower than that in the other two typical episodes. Yet, these emission reductions are still not enough to eliminate regional heavy pollution episodes. Compared with the actual emission levels during January to February 2020, a 20% extra reduction in air pollutant emissions in the "2+26" cities (or a 45% extra reduction compared with baseline emission levels of the autumn and winter of 2019 to 2020) could help to generally eliminate regionwide severe pollution episodes, and avoid heavy pollution episodes that last three or more consecutive days in Beijing; a 40% extra reduction in emissions (or a 60% extra reduction compared with baseline emission levels of the autumn and winter of 2019 to 2020) could help to generally eliminate regionwide and continuous heavy pollution episodes. Our analysis finds that during the clean period after the heavy pollution episode around the 2020 Spring Festival, the regionwide heavy pollution episode would only occur with at least a 10-fold increase in air pollutant emissions.

[Time-Determination and Contribution Analysis of Transport, Retention, and Offshore Backflow to Long-Term Sand-Dust Coupling].

Continuous on-line observation of particulate matter and PM2.5 chemical composition was conducted from October 15th to November 7th 2019 in East China. During the observation period, a wide range of dust-related processes took place. According to supplementary urban air quality assessment affected by dust (hereafter referred to as supplementary provisions), the observations were divided into four stages including pre-dust event, dust Ⅰ, dust Ⅱ, and post-dust event. The dust Ⅰ stage represented the processes of transportation and retention, while the dust Ⅱ stage represented processes of backflow from the sea and scavenging. The start time of the studied dust event was October 29th 08:00-09:00 based on the supplementary provisions, dust tracers, and air quality models; however, disagreements existed between these data sources with respect to the finishing time. The supplementary provisions could not effectively distinguish backflow dust from sea, and results from different dust tracers were variable. The WRF-CMAQ model simulated dust variation trends well but overestimated short-term suspended dust and backflow dust. PM10, PM2.5, and trace element concentrations were much higher during dust events than during non-dust periods, with highest daily concentrations of (234.8±125.5), (76.8±22.5), and (17.54±10.5) µg·m-3, respectively, which occurred on October 29th. During the dust event, concentration of crustal elements were remarkably high in PM2.5. At the same time, secondary ions (SO42-, NO3-, and NH4+) contributed less to PM2.5 mass concentrations. Four major crustal elements (Al, Si, Ca, and Fe) accounted for 23.5% and 13.7% of the mass concentration of PM2.5 and secondary ions accounted for 24.3% and 41.9% during dust Ⅰ and dust Ⅱ stages, respectively. Based on PMF source apportionment, Ca abundance, PM2.5/PM10 in dust sources, and the reconstruction of crustal material, dust particulates accounted for 43.4%-50.0% of PM2.5 and backflow dust accounted for 19.2%-24.7% of PM2.5.

[Modeling Studies of Source Contributions to PM2.5 in Chengdu, China].

This study establishes eight emission scenarios in the air pollutant emissions inventory of Chengdu City, China. We use the Weather Research and Forecasting and Community Multiscale Air Quality (WRF-CMAQ) models and a "zero-out" approach to investigate contributions of air pollution transport and sources to aerosol fine particulate matter (PM2.5) pollution in Chengdu City during January, April, July, and October 2015. The results showed that PM2.5 pollution in Chengdu City was serious during these months and reached >130 µg·m-3 in January. Highest concentrations were measured in the city center. PM2.5 pollution in Chengdu and the surrounding cities was found to exhibit regional characteristics. Since the air mass was stable during the monitoring periods, the interregional transmission capability of air pollution was poor, and thus local sources were the main contributors (61% of the annual average concentration) to PM2.5 pollution in Chengdu City. The contributions of local sources in April and July were higher than of those in January and October. We found that the main sources of PM2.5 pollution in Chengdu City were automobile emission (29% of the total), dust (26%), and domestic pollution (24%), and should be further controlled in the future.