Ozone and its precursors at an urban site in the Yangtze River Delta since clean air action plan phase II in China

In response to regional ozone (O3) pollution, Chinese government has implemented air pollution control measures in recent years. Here, a case study was performed at an O3-polluted city, Wuhu, in Yangtze River Delta region of China to investigate O3 variation trend and the relationship to its precursors after implementation of Clean Air Action Plan Phase II, which aims to reduce O3 pollution. The results showed that peak O3 concentration was effectively reduced since Clean Air Action Plan Phase II. Due to significant NOx reduction, O3 formation tended to shift from volatile organic compound (VOC)-limited regimes to NOx-limited regimes during 2018-2022. VOC/NOx ratios measured in 2022 revealed that peak O3 concentration tended to respond positively to NOx. Apart from high-O3 period, Wuhu was still in a VOC-limited regime. The relationship of maximum daily 8-h ozone average and NO2 followed a lognormal distribution with an inflection point at 20 μg m-3 of NO2, suggesting that Wuhu should conduct joint control of VOC and NOx with a focus on VOC reduction before the inflection point. Alkenes and aromatics were suggested to be preferentially controlled due to their higher ozone formation potentials. Using random forest meteorological normalization method, meteorology had a positive effect on O3 concentration in 2018, 2019 and 2022, but a negative effect in 2020 and 2021. The meteorology could explain 44.0 ± 19.1% of the O3 variation during 2018-2022. High temperature favors O3 production and O3 pollution occurred more easily when temperature was over 25 °C, while high relative humidity inhibits O3 generation and no O3 pollution was found at relative humidity above 70%. This study unveils some new insights into the trend of urban O3 pollution in Yangtze River Delta region since Clean Air Action Plan Phase II and the findings provide important references for formulating control strategies against O3 pollution.

Diurnal variation characteristics and meteorological causes of autumn ozone in the Pearl River Delta, China

This study investigates the diurnal variation of ozone (O3) in the Pearl River Delta (PRD) during autumn from 2016 to 2021, focusing on the main O3 modes and their relationship with meteorological conditions. Utilizing K-means clustering, four patterns of O3 variation were identified: Cluster 1 (extremely low O3), Cluster 2 (close to autumn average), Cluster 3 (abnormally high O3 at night), and Cluster 4 (extremely high O3). In Cluster 1, the PRD was situated on the northwest side of the western Pacific subtropical high (WPSH), resulting in increased cloud cover, weakened radiation, and the lowest O3 growth rate during the day, with weak nighttime changes. Cluster 2 presents O3 changes under normal autumn conditions, closely resembling the autumn average. In Cluster 3, the PRD was located between continental high pressure and the low-pressure system over the South China Sea. The enhanced horizontal pressure gradient led to an increase in the horizontal wind speed, promoting the formation of a low-level jet (LLJ). The LLJ caused decoupling between the residual layer and stable boundary layer at night, leading to increased surface O3 concentration and a higher background O3 concentration before sunrise the next day. In Cluster 4, favorable meteorological conditions for O3 generation and accumulation were created by the influence of the WPSH and peripheral tropical cyclones. O3 rapidly increased during the day, reaching extremely high values in the afternoon, with an exceedance rate of 80 %. Comparing the four diurnal patterns and their meteorological conditions highlights the significance of meteorological processes in O3 variations.

Meteorological influences on daily variation and trend of summertime surface ozone over years of 2015-2020: Quantification for cities in the Yangtze River Delta

We quantify the meteorological influences on daily variations and trends of maximum daily 8-h average ozone (MDA8 O₃) concentrations by using multiple linear regression (MLR) and Lindeman, Merenda, and Gold (LMG) approaches. Different from previous region-based studies, we pay special attention to meteorological influences at city scale. Over 2015-2019, daily changes in key meteorological parameters could explain 47%-74% of the observed daily variations in summertime MDA8 O₃ concentrations in Yangtze River Delta (YRD) and four cities (Shanghai, Nanjing, Hangzhou, and Hefei), with RH being the top driver. Over years of 2015-2020, daily concentrations of MDA8 O₃ obtained from MLR equations (MDA8O₃_MLR) of the local cities always had better performance than those of YRD. Compared with the observed daily MDA8 O₃ in June-July-August (JJA) over the studied period, daily MDA8O₃_MLR of the local cities (of YRD) had correlation coefficients of 0.73 (0.63), 0.75 (0.74), 0.79 (0.78), and 0.76 (0.73) in Shanghai, Nanjing, Hangzhou, and Hefei, respectively, and the MDA8O₃_MLR of the local cities (of YRD) captured 54% (17%), 63% (51%), 52% (27%) of the observed O₃-polluted days (days with MDA8 O₃ concentration exceeding 160 μg m^-3) in Shanghai, Nanjing, and Hangzhou, respectively. The meteorologically driven trends (Trend_Met) in MDA8 O₃ were calculated using the established MLR equations. Over 2015-2019, the observed trends (Trend_Obs) and Trend_Met in MDA8 O₃ were mostly positive in YRD, Nanjing, Hangzhou, and Hefei. In Shanghai, Trend_Obs, Trend_Met, and anthropogenically driven trend (estimated as Trend_Obs minus Trend_Met) of MDA8 O₃ in JJA over 2015-2019 were -1.3, +1.0, and -2.3 μg m^-3 y^-1, respectively, indicating that the emission control measures alleviated O₃ pollution in this city. Our results suggest that it is necessary to establish MLR equations at city scale to account for the role of meteorology in the actions of O₃ pollution control.

Evaluating the real changes of air quality due to clean air actions using a machine learning technique: Results from 12 Chinese mega-cities during 2013-2020

China has implemented two national clean air actions in 2013-2017 and 2018-2020, respectively, with the aim of reducing primary emissions and hence improving air quality at a national level. It is important to examine the effectiveness of such emission reductions and assess the resulting changes in air quality. However, such evaluation is difficult as meteorological factors can amplify, or obscure the changes of air pollutants, in addition to the emission reduction. In this study, we applied the random forest machine learning technique to decouple meteorological influences from emissions changes, and examined the deweathered trends of air pollutants in 12 Chinese mega-cities during 2013-2020. The observed concentrations of all criteria pollutants except O₃ showed significant declines from 2013 to 2020, with PM_2.5 annual decline rates of 6-9% in most cities. In contrast, O₃ concentrations increased with annual growth rates of 1-9%. Compared with the observed results, all the pollutants showed smoothed but similar variation in trend and annual rate-of-change after weather normalization. The response of O₃ to NO₂ concentrations indicated significant regional differences in photochemical regimes, and the differences between observed and deweathered results provided implications for volatile organic compound emission reductions in O₃ pollution mitigation. We further evaluated the effectiveness of first and second clean air actions by removing the meteorological influence. We found that the meteorology can make negative or positive contribution in reducing pollutant concentrations from emission reduction, depending on type of pollutants, locations, and time period. Among the 12 mega-cities, only Beijing showed a positive meteorological contribution in amplifying reductions in main pollutants except O₃ during both clean air action periods. Considering the large and variable impact of meteorological effects in changing air quality, we suggest that similar deweathered analysis is needed as a routine policy evaluation tool on a regional basis.

Long-term variation of boundary layer height and possible contribution factors: A global analysis

Boundary layer height (BLH) plays an important role in regulating global weather/climate, as well as the dispersion and transportation of pollutants. Until now, however, the attribution and contributions of different controlling factors to BLH long-term variability and trends have not been quantified on a global scale. The long-term radiosonde dataset was used in this study to retrieve global BLH climatology; seasonal, diurnal, long-term variation and trends were analyzed over a 39-year period (1980-2018). Statistical results show that the global distribution of the BLH and its trend have apparent day-night differences. BLH during daytime is deeper during clear-sky conditions compared to cloudy sky conditions, indicating a significant effect of clouds; BLH during nighttime is deeper under cloudy conditions. BLH was also found to vary over different land types; dry and hot soil exhibits a deeper BLH than those of wet and cool soil. The long-term variation and trend of BLH are highly influenced by near-surface meteorological parameters. In particular, based on multiple linear stepwise regression models and the contribution calculation method, this investigation initiatively quantifies the influences of meteorological parameters on global BLH long-term variation and trend. Our results emphasized that a 10 m wind speed (WS) and low tropospheric stability (LTS) have significant contributions to long-term BLH variation; WS and LTS anomalies alternately dominated the contribution of the diurnal cycle of the BLH anomaly. Annual BLH recorded an average increasing trend (38.9-42.1 m/decade), and LTS is more dominant than WS from a contribution perspective, especially for increased BLH anomaly. Contributions from near-surface temperature (T) and relative humidity (RH) also play important roles. However, a decreasing WS trend dominated the decreased trends of BLH anomaly, accounting for nearly 40% of the total contribution.

Meteorological influences on PM2.5 and O3 trends and associated health burden since China's clean air actions

Stringent clean air actions have been implemented to improve air quality in China since 2013. In addition to anthropogenic emission abatements, the changes in air quality may be modulated also by meteorology. In this study, we developed multiple linear regression models to quantify meteorological influences on the trends in fine particulate matter (PM_2.5) and ozone (O₃) concentrations and associated health burden over three polluted regions of China, i.e., North China Plain, Yangtze River Delta, and Fen-wei Plain during 2014-2018, with a novel focus on the contributions of the most influential meteorological factors to PM_2.5 and O₃ trends as well as the meteorological contributions to PM_2.5- and O₃-related mortality trends. The meteorology-driven PM_2.5 (O₃) trends for the three regions were -0.5~-2.0 (+0.7~+0.8) μg m^-3 yr^-1, contributing 10- 26% (12- 18%) of the observed five-year decreasing PM_2.5 (increasing O₃) trends. The decreased relative humidity (increased daytime planetary boundary layer height) was identified to be the most influential meteorological factor and explained 55% (42%) of the largest meteorology-driven PM_2.5 (O₃) trend among all regions and seasons. The meteorology-driven decreases in PM_2.5 (increases in O₃) concentrations led to overall decreases in PM_2.5-related (increases in O₃-related) mortalities with trends of -2.2~-7.4 (+0.5~+0.9) thousand yr^-1 for the three regions, accounting for 10- 26% (15- 31%) of the total decreasing (increasing) trends in PM_2.5-related (O₃-related) mortalities. The results emphasize the important role of meteorology in PM_2.5 and O₃ air quality and associated health burden over China, and have important implications for China's air quality planning. In particular, more efforts in emission control should be taken to offset the adverse effects on ozone caused by meteorology.

Characteristics and formation of typical winter haze in Handan, one of the most polluted cities in China

Handan, a city within the North China Plain (NCP) region, is a typical city influenced by regional particulate matter (PM) pollution. One-year hourly semi-continuous observation was carried out in 2015 in Handan with the aim of identifying the chemical composition and variations in PM_2.5. Moreover, the concentration of aerosol precursors, meteorological factors, and secondary transformations are considered. The results demonstrate that the annual average PM_2.5 concentration in Handan is 122.35μgm^-3, approximately 3.5 times higher than the Chinese National Ambient Air Quality Standard (NAAQS) (35μgm^-3), and only 12days were below the guideline. As expected, PM concentrations are highest in winter, especially in December. In addition, we measure the concentrations of five species commonly found in PM, nitrate, sulfate, ammonium, inorganic carbon, and organic carbon. Of these, nitrate and sulfate account for the largest proportion of PM_2.5; during periods when the PM_2.5 concentration was below 400μgm^-3, nitrate dominates, while above this concentration, sulfate dominate. This is likely related to the nitrogen and sulfur oxidation ratios, which are in turn, especially the sulfur oxidation ratio, driven by high relative humidity (>60%). In addition, haze events are driven by other meteorological conditions, wind speed and direction, where low wind speeds from the south and southwest enable pollutant accumulation, which are infrequently interspersed with brief periods with high wind speeds that promote pollutant dispersal. Even though Handan is among the ten most polluted cities in China with regard to air pollution, few studies beyond model simulations have analyzed air pollutant concentrations in this city. Therefore, this study makes a significant contribution to understanding air pollution in Handan, which can further be used to improve our understanding of regional pollution in the highly populated North China Plain. These results have implications for the creation of policies and legislation, as well as other pollution control measures.