Mechanisms and Pathways for Coordinated Control of Fine Particulate Matter and Ozone

Severe photochemical pollution was found in large petrochemical complexes: A typical case study in North China

Large petrochemical complex (PC) widely exists in both developing and developed countries, and is expected to have a special photochemical pollution in local scale due to huge VOCs emissions. Here, a typical large-scale PC in North China was selected as the study case, to explore the character, formation and influence of local photochemical pollution regarding PCs based on an improved 0-D chemical model. In the study PC, VOCs-rich character was apparent with THCs level of 90.8 ± 28.0 ppb and THCs/NOx ratio of ∼26.2 mol/mol. Severe O3 pollution was found in warm months with monthly mean MDA1O3 of 67.3-96.0 ppb. Model simulations showed the heavy O3 pollution in this PC was attributed to high precursors rather than to unfavorable meteorology, and was more sensitive to NOx (with response of 1.42 g/g) than to THCs (with response of 0.12 g/g). The photochemical pollution formation potential of the emission plumes of this PC was very enormous, with production rate of 19.6 ppb h-1 for O3, 2.9 ppb h-1 for HCHO and 1.1 ppb h-1 for CH3CHO on daytime average, 1-5 greater than in normal urban areas. The higher production rates happened in morning hours, which explained the earlier peak time of observed O3 in PCs. And about 70% of photochemical pollution (represented by O3) would be transported to surroundings, leading to the significant photochemical-pollution hazard to the vicinity of PCs.

Association of Short-Term Co-Exposure to Particulate Matter and Ozone with Mortality Risk

A complex regional air pollution problem dominated by particulate matter (PM) and ozone (O3) needs drastic attention since the levels of O3 and PM are not decreasing in many parts of the world. Limited evidence is currently available regarding the association between co-exposure to PM and O3 and mortality. A multicounty time-series study was used to investigate the associations of short-term exposure to PM1, PM2.5, PM10, and O3 with daily mortality from different causes, which was based on data obtained from the Mortality Surveillance System managed by the Jiangsu Province Center for Disease Control and Prevention of China and analyzed via overdispersed generalized additive models with random-effects meta-analysis. We investigated the interactions of PM and O3 on daily mortality and calculated the mortality fractions attributable to PM and O3. Our results showed that PM1 is more strongly associated with daily mortality than PM2.5, PM10, and O3, and percent increases in daily all-cause nonaccidental, cardiovascular, and respiratory mortality were 1.37% (95% confidence interval (CI), 1.22-1.52%), 1.44% (95% CI, 1.25-1.63%), and 1.63% (95% CI, 1.25-2.01%), respectively, for a 10 μg/m3 increase in the 2 day average PM1 concentration. We found multiplicative and additive interactions of short-term co-exposure to PM and O3 on daily mortality. The risk of mortality was greatest among those with higher levels of exposure to both PM (especially PM1) and O3. Moreover, excess total and cardiovascular mortality due to PM1 exposure is highest in populations with higher O3 exposure levels. Our results highlight the importance of the collaborative governance of PM and O3, providing a scientific foundation for pertinent standards and regulatory interventions.

Cooperative simultaneous inversion of satellite-based real-time PM2.5 and ozone levels using an improved deep learning model with attention mechanism

Ground-level fine particulate matter (PM_2.5) and ozone (O₃) are air pollutants that can pose severe health risks. Surface PM_2.5 and O₃ concentrations can be monitored from satellites, but most retrieval methods retrieve PM_2.5 or O₃ separately and disregard the shared information between the two air pollutants, for example due to common emission sources. Using surface observations across China spanning 2014-2021, we found a strong relationship between PM_2.5 and O₃ with distinct spatiotemporal characteristics. Thus, in this study, we propose a new deep learning model called the Simultaneous Ozone and PM_2.5 inversion deep neural Network (SOPiNet), which allows for daily real-time monitoring and full coverage of PM_2.5 and O₃ simultaneously at a spatial resolution of 5 km. SOPiNet employs the multi-head attention mechanism to better capture the temporal variations in PM_2.5 and O₃ based on previous days' conditions. Applying SOPiNet to MODIS data over China in 2022, using 2019-2021 to construct the network, we found that simultaneous retrievals of PM_2.5 and O₃ improved the performance compared with retrieving them independently: the temporal R² increased from 0.66 to 0.72 for PM_2.5, and from 0.79 to 0.82 for O₃. The results suggest that near-real time satellite-based air quality monitoring can be improved by simultaneous retrieval of different but related pollutants. The codes of SOPiNet and its user guide are freely available online at https://github.com/RegiusQuant/ESIDLM.