Objective weather classification methods have been extensively applied to identify dominant ozone-favorable synoptic weather patterns (SWPs), however, the consistency of different classification methods is rarely examined. In this study, we apply two widely-used objective methods, the self-organizing map (SOM) and K-means clustering analysis, to derive ozone-favorable SWPs at four Chinese megacities in 2015-2022. We find that the two algorithms are largely consistent in recognizing dominant ozone-favorable SWPs for four Chinese megacities. In the case of classifying six SWPs, the derived circulation fields are highly similar with a spatial correlation of 0.99 between the two methods, and the difference in the mean frequency of each SWP is less than 7%. The six dominant ozone-favorable SWPs in Guangzhou are all characterized by anomaly higher radiation and temperature, lower cloud cover, relative humidity, and wind speed, and stronger subsidence compared to climatology mean. We find that during 2015-2022, the occurrence of ozone-favorable SWPs days increases significantly at a rate of 3.2 day/year, faster than the increases in the ozone exceedance days (3.0 day/year). The interannual variability between the occurrence of ozone-favorable SWPs and ozone exceedance days are generally consistent with a temporal correlation coefficient of 0.6. In particular, the significant increase in ozone-favorable SWPs in 2022, especially the Subtropical High type which typically occurs in September, is consistent with a long-lasting ozone pollution episode in Guangzhou during September 2022. Our results thus reveal that enhanced frequency of ozone-favorable SWPs plays an important role in the observed 2015-2022 ozone increase in Guangzhou.

High-concentration ozone pollution pose threats to ecosystems and human health. However, there is limited research on the impact of the alternating evolution of synoptic weather patterns (SWPs) on the multi-scale transport processes and sources of ozone. From June 14 to 18, 2018, a rare consecutive ozone pollution plagued in Hefei and broader Yangtze River Delta region (YRD). This study investigates the meteorological factors and sources using in-situ observational data and WRF-Chem model simulations. Analysis reveals a northeastern low-pressure system moving from north to south generated a cold front. This moving cold front facilitated the vertical transport of warm air masses carrying high-concentration ozone originating from North China. Subsequently, Ozone-rich air masses (ORMs) were transported over the YRD, influenced by the eastward movement of the Mongolian high-pressure system. Based on WRF-Chem model with NOx tagging mechanisms and WRF-FLEXPART backward simulations, it is confirmed that a notable atmospheric transport originated from North China region (NCR) to Hefei, especially on June 15. As the Mongolian high-pressure weakens and shifts east-southward, it carried ORMs generated by NOx emissions from the YRD, accumulating over the sea within the range of 120°E to 126°E and 25°N to 30°N. Both WRF-chem model results and TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) Chemistry Reanalysis dataset Version 2 (TCR-2) revealed the existence of ORMs in this geographic range. Subsequently, the ORMs carried out to sea by the weakened high-pressure system were reintroduced inland, influenced by southeast winds brought about by the peripheral circulation of typhoon \"Gaemi\". In summary, the alternating evolution of SWPs significantly influences multi-scale ozone transport from both the NCR and the YRD regions, making substantial contributions to this prolonged episode. These findings offer valuable insights for improving regional ozone pollution prevention and control mechanisms.

There is considerable academic interest in the particle-ozone synergistic relationship (PO) between fine particulate matter (PM2.5) and ozone (O3). Using various synoptic weather patterns (SWPs), we quantitatively assessed the variations in the PO, which is relevant to formulating policies aimed at controlling complex pollution in the air. First, based on one-year sampling data from March 2018 to February 2019, the SWPs classification of the Yangtze River Delta (YRD) was conducted using the sum-of-squares technique (SS). Five dominant SWPs can be found in the YRD region, including the Aleutian low under SWP1 (occurring 45 % of the year), a tropical cyclone under SWP2 (21 %), the tropical cyclone and western Pacific Subtropical High (WPSH) under SWP3 (15.4 %), the WPSH under SWP4 (6.9 %), and a continental high pressure under SWP5 (3.1 %). The phenomenon of a \"seesaw\" between PM2.5 and O3 concentrations exhibited significant spatial heterogeneity, which was influenced by meteorological mechanisms. Second, the multi-linear regression (MLR) model and the partial correlation (PCOR) analysis were employed to quantify the effects of dominant components and meteorological factors on the PO. Meteorological variables could collectively explain only 33.0 % of the PM2.5 variations, but 58.0 % for O3. O3 promoted each other with low concentrations of PM2.5 but was inhibited by high concentrations of PM2.5. High relative humidity (RH) was conducive to the generation of PM2.5 secondary components and enhanced the radiative effects of aerosols and the negative correlation of PO. In addition, attention should be paid to assessing the combined effects of precursor levels, weather, and chemical reactions on the particle-ozone complex pollution. The control of O3 pollutants should be intensified in summer, while the focus should be on reducing PM2.5 pollutants in winter. Prevention and control measures need to reflect the differences in weather conditions and pollution characteristics, with a focus on RH and secondary components of PM2.5.

To clarify the mechanism underlying the effects of weather patterns and topography on air pollution, this study conducted the obliquely rotated principal component analysis in the T-mode to analyze ERA5 reanalysis data and categorize typical weather patterns at a 700-hPa geopotential height from 2015 to 2022. The probability of worsened air pollution attributable to weather patterns was quantitatively assessed using a generalized additive model. The results indicated that due to the influence of topography, Lanzhou was affected by an extended period of downdraft (with weak convective intensity) and the delayed formation of a convective boundary layer during the daytime by 1-2 h relative to other areas. Under the combined effect of low trough patterns (south low pressure type [SL] and south low weak pressure type [SL-]) and topography, the formation of a stable layer above the planetary boundary layer (PBL) would weaken the vertical exchange of the local airflow and inhibit the development of the PBL. The type of SL led to the most severe pollution, causing a 61.9 % (95 % confidence interval [CI]: 46.3 %-79.3 %) increase in PM2.5 concentration. For southwest high pressure patterns (south high [SH], southwest weak high [SWH-], southwest high [SWH], and southwest strong high [SWH+] pressure types), the prevailing northwest wind was the main transport path for pollutants. For the high pressure patterns (north high [NH] and northwest high [NWH] pressure types) and south wind patterns (southeast weak high [SEH-], southeast high [SEH], and northeast high [NEH] pressure types), the enhancement of vertical convection, deepening of the PBL, and reduction of pollution transport led to improved air quality. The NH, NWH, and NEH pressure types caused PM2.5 concentration to decrease by 18.4 % (95 % CI: 8.8 %-27.1 %), 14.9 % (95 % CI: 4.7 %-24.0 %), and 35.9 % (95 % CI: 9.7 %-54.6 %), respectively.

The Beijing-Tianjin-Hebei (BTH) region in China has been frequently suffering from severe haze events (observed daily mean surface fine particulate matter PM2.5 concentrations larger than 150 μg m-3) partially caused by certain types of large-scale synoptic patterns. Black carbon (BC), as an important PM2.5 component and a primarily emitted species, is a good tracer for investigating sources and formation mechanisms leading to severe haze pollutions. We apply GEOS-Chem model and its adjoint to quantify the source contributions to BC concentrations at the surface and at the top of the planetary boundary layer (PBL) during typical types of severe haze events for April 2013-2017 in BTH. Four types of severe haze events, mainly occurred in December-January-February (DJF, 62.3%) and in September-October-November (SON, 26.3%), are classified based on the associated synoptic weather patterns using principal component analysis. Model results reasonably capture the daily variations of BC measurements at three ground sites in BTH. The adjoint method attributes BC concentrations to emissions from different source sectors and from local versus regional transport at the model spatial and temporal resolutions. By source sectors, the adjoint method attributes the daily BC concentrations during typical severe haze events (in winter heating season) in Beijing largely to residential emissions (48.1-62.0%), followed by transportation (16.8-25.9%) and industry (19.1-29.5%) sectors. In terms of regionally aggregated source influences, local emissions in Beijing (59.6-79.5%) predominate the daily surface BC concentrations, while contributions of emissions from Beijing, Hebei, and outside BTH regions are comparable to the daily BC concentrations at the top of PBL (~200-400 m). Our adjoint analyses would provide a scientific support for joint regional and targeted control policies on effectively mitigating the particulate pollutions when the dominant synoptic weather patterns are predicted.

North China Plain area (NCP) is one of the most densely populated and heavily polluted regions in the world. In the last five years, frequently happened fine particulate matter (PM2.5) serious pollution events were one of the top environmental concerns in China. As PM2.5 concentrations are highly influenced by synoptic flow patterns and local meteorological conditions, a two-stage hierarchical clustering method based on dynamic principal component analysis (DPCA) and standard k-means clustering algorithm was employed to classify synoptic wind fields into 6 patterns over the NCP area using the data of 5 PM2.5 seasons (Sept. 15th-Apr. 15th) from 2013 to 2017. Among the six identified synoptic patterns, pattern of uniform pressure field (U) and that of zonal high pressure (ZH) accounted for 78.21%, 65.55%, 63.56%, 57.11%, 59.13% and 58.27% studied heavy smog pollution events in Beijing, Tianjin, Tangshan, Baoding, Shijiazhuang and Xingtai city. The two particular patterns were associated with uniform pressure field and sparsely latitudinal isobar in 850 hPa level, respectively. They were also characterized by high relative humidity, low temperature, low-speed northerly wind in Tianjin and Tangshan, and southerly wind in the other cities. Under the continuous control of pattern ZH, the values of 24 h-average PM2.5 were found to increase at a rate of 31.78 μg/m3 per day. To evaluate the contribution of meteorological factors and precursors to PM2.5 levels, linear mixed-effects models (LMMs) were applied to establish relations among 24 h-average PM2.5 concentrations, concentrations of main precursors, local meteorological factors and synoptic patterns. Results show that the variations of precursors, local meteorological factors and synoptic flow patterns can explain 51.67%, 19.15% and 14.01% changes of the 24 h-average PM2.5 concentrations, respectively. This study illustrates that dense precursor emissions are still the main cause for heavy haze pollution events, although meteorological conditions play almost equal roles sometimes.

The aim of the study is to assess the relationship between PM2.5, synoptic weather patterns, and admissions for circulatory and respiratory disease. A PM2.5 event is defined as a day when the daily mean PM2.5 concentration exceeds 65 μg/m(3). PM2.5 events that coincided with the occurrence of PM attributed to Asian dust storm (ADS) and photochemical smog (PCS) were removed from the study in order to focus solely on the health effects from PM2.5. A one-tailed z-test and a relative risk (RR) estimate were performed. Hospital admissions for respiratory diseases were greater than those for circulatory diseases, and asthma-related diseases had a higher impact in the Adults group, and the maximum RR was 1.94 [1.37 2.77] on the first day after the event. It is evident that PM2.5 episodes connected to particular synoptic weather patterns pose a risk to health as large as ADS and PCS events.