Ground-level ozone (O₃) pollution has been steadily getting worse in most parts of eastern China during the past 5 years. The non-linearity of O₃ formation with its precursors like nitrogen oxides (NO_x= NO + NO₂) and volatile organic compounds (VOCs) are complicating effective O₃ abatement plans. The diagnosis from space-based observations, i.e. the ratio of formaldehyde (HCHO) columns to tropospheric NO₂ columns (HCHO $/$ NO₂), has previously been proved to be highly consistent with our current understanding of surface O₃ chemistry. HCHO $/$ NO₂ ratio thresholds distinguishing O₃ formation sensitivity depend on regions and O₃ chemistry interactions with aerosol. To shed more light on the current O₃ formation sensitivity over China, we have derived HCHO $/$ NO₂ ratio thresholds by directly connecting satellite-based HCHO $/$ NO₂ observations and ground-based O₃ measurements over the major Chinese cities in this study. We find that a VOC-limited regime occurs for HCHO $/$ NO₂ < 2.3, and a NO_x-limited regime occurs for HCHO $/$ NO₂ > 4.2. The HCHO $/$ NO₂ between 2.3 and 4.2 reflects the transition between the two regimes. Our method shows that the O₃ formation sensitivity tends to be VOC-limited over urban areas and NO_x-limited over rural and remote areas in China. We find that there is a shift in some cities from the VOC-limited regime to the transitional regime that is associated with a rapid drop in anthropogenic NO_x emissions, owing to the widely applied rigorous emission control strategies between 2016 and 2019. This detected spatial expansion of the transitional regime is supported by rising surface O₃ concentrations. The enhanced O₃ concentrations in urban areas during the COVID-19 lockdown in China indicate that a protocol with simultaneous anthropogenic NO_x emissions and VOC emissions controls is essential for O₃ abatement plans.

Elevated ground-level ozone (O₃), which is an important aspect of air quality related to public health, has been causing increasing concern. This study investigated the spatiotemporal distribution of ground-level O₃ concentrations in China using a dataset from the Chinese national air quality monitoring network during 2013–2015. This research analyzed the diurnal, monthly and yearly variation of O₃ concentrations in both sparsely and densely populated regions. In particular, 6 major Chinese cities were selected to allow a discussion of variations in O₃ levels in detail, Beijing, Chengdu, Guangzhou, Lanzhou, Shanghai, and Urumchi, located on both sides of the Heihe-Tengchong line. Data showed that the nationwide 3-year MDA8 of ground-level O₃ was 80.26 μg/m³. Ground-level O₃ concentrations exhibited monthly variability peaking in summer and reaching the lowest levels in winter. The diurnal cycle reached a minimum in morning and peaked in the afternoon. Yearly average O₃ MDA8 concentrations in Beijing, Chengdu, Lanzhou, and Shanghai in 2015 increased 12%, 25%, 34%, 22%, respectively, when compared with those in 2013. Compared with World Health Organization O₃ guidelines, Beijing, Chengdu, Guangzhou, and Shanghai suffered O₃ pollution in excess of the 8-hour O₃ standard for more than 30% of the days in 2013 to 2015.

Air pollution has aroused significant public concern in China, therefore, long-term air-quality data with high temporal and spatial resolution are needed to understand the variations of air pollution in China. However, the yearly variations with high spatial resolution of air quality and six air pollutants are still unknown for China until now. Therefore, in this paper, we analyze the spatial and temporal variations of air quality and six air pollutants in 366 cities across mainland China during 2015–2017 for the first time to the best of our knowledge. The results indicate that the annual mean mass concentrations of PM2.5, PM10, SO₂, and CO all decreased year by year during 2015–2017. However, the annual mean NO₂ concentrations were almost unchanged, while the annual mean O₃ concentrations increased year by year. Anthropogenic factors were mainly responsible for the variations of air quality. Further analysis suggested that PM2.5 and PM10 were the main factors influencing air quality, while NO₂ played an important role in the formation of PM2.5 and O₃. These findings can provide a theoretical basis for the formulation of future air-pollution control policy in China.

The TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor satellite has been used to detect the atmospheric environment since 2017, and it is of great significance to investigate the accuracy of its products. In this work, we present comparisons between TROPOMI tropospheric NO₂ and total SO₂ products against ground-based MAX-DOAS at a single site (Xianghe) and OMI products over a seriously polluted region (North China Plain, NCP) in China. The results show that both NO₂ and SO₂ data from three datasets exhibit a similar tendency and seasonality. In addition, TROPOMI tropospheric NO₂ columns are generally underestimated compared with collocated MAX-DOAS and OMI data by about 30–60%. In contrast to NO₂, the monthly average SO₂ retrieved from TROPOMI is larger than MAX-DOAS and OMI, with a mean bias of 2.41 (153.8%) and 2.17 × 10¹⁶ molec cm⁻² (120.7%), respectively. All the results demonstrated that the TROPOMI NO₂ as well as the SO₂ algorithms need to be further improved. Thus, to ensure reliable analysis in NCP area, a correction method has been proposed and applied to TROPOMI Level 3 data. The revised datasets agree reasonably well with OMI observations (R > 0.95 for NO₂, and R > 0.85 for SO₂) over the NCP region and have smaller mean biases with MAX-DOAS. In the application during COVID-19 pandemic, it showed that the NO₂ column in January-April 2020 decreased by almost 25–45% compared to the same period in 2019 due to the lockdown for COVID-19, and there was an apparent rebound of nearly 15–50% during 2021. In contrast, a marginal change of the corresponding SO₂ is revealed in the NCP region. It signifies that short-term control measures are expected to have more effects on NO₂ reduction than SO₂; conversely, we need to recognize that although the COVID-19 lockdown measures improved air quality in the short term, the pollution status will rebound to its previous level once industrial and human activities return to normal.