[Study of Peak Carbon Emission of a City in Yangtze River Delta Based on LEAP Model].

Based on the LEAP model framework, a LEAP-X sub-sector calculation model suitable for X City was constructed in this study. Four scenarios including a baseline scenario, low-carbon scenario, enhanced low-carbon scenario, and peak in 2023 scenario were set up to predict and analyze the carbon emission situation. The calculation and analysis results showed that it could achieve the carbon peak before 2030 only under the enhanced low-carbon scenario and peak in 2023 scenario. The peak year of the enhanced low-carbon scenario was around 2025 with a peak carbon emission of approximately 170 million tons, but the peak time may actually be delayed. Industry was the largest sector of carbon emissions, and the petrochemical industry was the largest portion of industry, the proportion of which was always maintained at approximately 30% under different scenarios. However, the proportion of power generation and steel industry decreased annually, whereas the proportion of the net imported power gradually increased. Industrial structure optimization and energy structure adjustment were the main driving factors of carbon peak in X City. Carbon emissions per unit of GDP will fall by approximately 41% in 2030 compared with that in 2020 under the enhanced low-carbon scenario.

[Elucidating the Impacts of Meteorology and Emission Changes on Concentrations of Major Air Pollutants in Major Cities in the Yangtze River Delta Region Using a Machine Learning De-weather Method].

This study applied a de-weather method based on a machine learning technique to quantify the contribution of meteorology and emission changes to air quality from 2015 to 2021 in four cities in the Yangtze River Delta Region. The results showed that the significant reductions in PM2.5, NO2, and SO2 emissions(57.2%-68.2%, 80.7%-94.6%, and 81.6%-96.1%, respectively) offset the adverse effects of meteorological conditions, resulting in lower pollutant concentrations. The meteorological contribution of maximum daily 8-h average O3(MDA8_O3) showed a stronger effect than that of others(23.5%-42.1%), and meteorological factors promoted the increase in MDA8_O3 concentrations(4.7%); however, emission changes overall resulted in a decrease in MDA8_O3 concentrations(-3.2%). NO2 and MDA8_O3 decreased more rapidly from 2019 to 2021, mainly because the emissions played a stronger role in reducing pollutant concentrations than from 2015 to 2018. However, emissions changes had weaker reduction effects on PM2.5 and SO2 from 2019 to 2021 than from 2015 to 2018. De-weather methods could effectively seperate the effects of meteorology and emission changes on pollutant trends, which helps to evaluate the real effects of emission control policies on pollutant concentrations.

[Analysis of O3 Pollution Characteristics, Formation Sensitivity, and Transport Impact in Southern Nanjing].

As one of the most important city clusters in China, surface ozone (O3) pollution in the Yangtze River Delta (YRD) Region has become a prominent air quality problem in recent years. Online observations of ambient O3, nitrogen oxides (NOx), and volatile organic compounds (VOCs) were conducted in southern Nanjing from July-September 2020 and April-May 2021. On this basis, the pollution characteristics of O3 were analyzed. The O3-VOCs-NOx sensitivity and the transport influence of its precursors were further discussed using models. The frequency statistics of the daily maximum 8-hour moving average (DMA-8h O3) and hourly concentration (O3-1h) showed that O3 pollution in southern Nanjing was more serious than that in urban areas. Three typical O3 pollution episodes were selected during the whole observation period, which included August 16th-27th, 2020; September 3rd-11th, 2020; and May 17th-25th, 2021. The O3-VOCs-NOx sensitivities in these three pollution episodes were analyzed using the observation-based model (OBM). The results of the relative incremental reactivity (RIR) and empirical kinetics modeling approach (EKMA) showed that the sensitivities of O3 formation in the three pollution episodes were in the NOx-limited regime, the transition regime, and the VOCs-limited regime, respectively. This difference in O3-VOCs-NOx sensitivity reflects that the site may have been influenced by transport. Therefore, the potential source contribution function (PSCF) and the concentration weighted trajectory (CWT) method were further used to analyze the potential source areas of NOx, VOCs, and O3 in these three pollution episodes. The results showed that there were obvious regional transport effects of NOx, VOCs, and O3 in these three pollution episodes. The location of potential sources differed in these three pollution episodes, which were affected by the eastern cities of the Lishui site; the urban area of Nanjing and eastern area of Anhui Province; and the urban area of Nanjing and the middle of the YRD Region, respectively. The impact of transport on O3 and its precursors also indicated the necessity of regional joint prevention and control of O3 pollution in the YRD Region.

[Transport Influence and Potential Sources of PM2.5 Pollution for Nanjing].

In this study, 24-hour backward trajectories of the air mass in Nanjing were calculated by using the HYSPLIT model with the NCEP global reanalysis data from March 2019 to February 2020. The backward trajectories combined with the hourly concentration data of PM2.5 were then utilized in the trajectory clustering analysis and potential pollution source analysis. The results showed that the average concentration of PM2.5 in Nanjing was(36±20) µg·m-3 during the study period, with 17 days exceeding the grade Ⅱ national ambient air quality standards (75 µg·m-3). PM2.5 concentration exhibited clear seasonal variation, with winter (49 µg·m-3)>spring (42 µg·m-3)>autumn (31 µg·m-3)>summer (24 µg·m-3). PM2.5 concentration was significantly positively correlated with surface air pressure but significantly negatively correlated with air temperature, relative humidity, precipitation, and wind speed. Based on the trajectories, seven transport routes were identified in spring, and six routes for the other seasons. The northwest and south-southeast routes in spring, southeast route in autumn, and southwest route in winter were the main pollution transport routes in each season, with the characteristics of short transport distance and slow air mass movement, indicating that local accumulation was one of the main reasons for the high value of PM2.5 in quiet and stable weather. The distance of the northwest route in winter was large, and the PM2.5 concentration was 58 µg·m-3, which was the 2nd highest concentration in all routes, indicating that the cities in the northeast of Anhui had a great transport influence on Nanjing PM2.5. The distribution of PSCF and CWT was relatively consistent, and the main potential source areas were mainly local and adjacent areas of Nanjing, indicating that PM2.5 control is needed to strengthen local control and carry out joint prevention and control with adjacent areas. Winter was most affected by transport, its main potential source area was located at the junction of northwest Nanjing and Chuzhou, and the main source origin was in Chuzhou; therefore joint prevention and control should be expanded to Anhui.

[Transport Influence and Potential Sources of Ozone Pollution for Nanjing During Spring and Summer in 2017].

In this study, the 24-hour backward trajectories of air mass at ground level(10 m)in Nanjing were calculated by using the HYSPLIT model with the NCEP global reanalysis data from April 1st to October 31st, 2017. The backward trajectories were then combined with the hourly concentration data of O3 in Nanjing for trajectories clustering analysis and potential pollution sources analysis. The results show that in 2017, the maximum daily 8 h running average O3 level in Nanjing was around 12-261 µg·m-3 with 58 days of O3 pollution in Nanjing, mainly in the spring and summer. The monthly variation of O3 showed a single peak, with the highest O3 concentration, as well as the most days exceeding the standard, occurring in June; the diurnal variation of O3 was unimodal and reached its peak around 14:00. A total number of 5136 trajectories were obtained by simulation, among which the exceeded trajectories accounted for approximately 10%. The exceedance trajectories in May and June were significantly higher, accounting for 60% of the total exceedance trajectories. Six ground-level air mass transporting pathways were identified through clustering analysis, from the NNE, NW, SW, SSE, SE, and NE directions. The SE and SSE directions with higher O3 levels were the dominant transport routes of O3 pollution, contributing to 23.33% and 20.76% of backward trajectories, respectively. As for the potential pollution source analysis, the area with high WCWT value distribution matched the WPSCF result, indicating that the potential sources of O3 pollution were mainly distributed in Changzhou, Wuxi, Suzhou, Huzhou, and other cities around Taihu Lake. Additionally, cities located around Nanjing, such as Taizhou, Ma'anshan, Wuhu, Chuzhou, Nantong, and Lianyungang, were considered the secondary potential sources. The results indicate that O3 pollution in Nanjing is a regional issue and its control requires joint prevention and control strategies in the Yangtze River Delta.

[Scenario Simulation Study Constrained by the Ambient Air Quality Standards in Nanjing].

With the constraint that all six major pollutants in Nanjing must meet the air quality standards by 2030, on the basis of the 2015 emission inventory, the CMAQ air quality model was used to conduct PM2.5 sensitivity tests, and scenario analysis was used to predict the emission inventory and the air quality of four emission reduction scenarios were simulated. Finally, the total control index under the constraint of meeting the standards was obtained. The results show that primary particulate matter (PPM) reduction is the most effective at reducing the concentration of PM2.5 in the atmosphere, on the basis of emission reduction in surrounding areas, PPM emission reduction accounts for 88% of the total reduction of the annual average concentration of PM2.5, followed by NH3, NOx, SO2, and VOCs, which contribute to 10.3%, 5.5%, 3.2%, and 0.5%, respectively. Compared to 2015, the reduction ratios of the major pollutants are between 22% and 53%. Controlling the activity level is more effective for SO2, NH3 and CO emissions reduction, while there is still more opportunity for NOx and VOCs end treatment. When the emissions of SO2, NOx, PM10, PM2.5, BC, OC, CO, VOCs, and NH3 are controlled to 2.43×104, 8.47×104, 9.42×104, 3.74×104, 0.19×104, 0.30×104, 26.56×104, 13.08×104, and 1.50×104 t, respectively, it is expected that the levels of the six pollutants in Nanjing can meet the national ambient air quality level 2 standards.