Understanding the spatiotemporal variability and trends of surface ozone over India

Research on the spatial and temporal patterns of ozone concentration and population health effects in the Central Plains Urban Agglomeration from 2017 to 2020

Fine particulate matter (PM2.5) and near-surface ozone (O3) are the main atmospheric pollutants in China. Long-term exposure to high ozone concentrations adversely affects human health. It is of great significance to systematically analyze the spatiotemporal evolution mechanism and health effects of ozone pollution. Based on the ozone data of 91 monitoring stations in the Central Plains Urban Agglomeration from 2017 to 2020, the research used Kriging method and spatial autocorrelation analysis to investigate the spatiotemporal variations of ozone concentration. Additionally, the study assessed the health effects of ozone on the population using the population exposure risk model and exposure-response relationship model. The results indicated that: (1) The number of premature deaths caused by ozone pollution in the warm season were 37,053 at 95% confidence interval (95% CI: 28,190-45,930) in 2017, 37,685 (95% CI: 28,669-46,713) in 2018, and 37,655 (95% CI: 28,647-46,676) in 2019. (2) The ozone concentration of the Central Plains urban agglomeration showed a decreasing trend throughout the year and during the warm season from 2017 to 2020, there are two peaks monthly, one is June, and the other is September. (3) In the warm season, the high-risk areas of population exposure to ozone in the Central Plains Urban Agglomeration were mainly concentrated in urban areas. In general, the population exposure risk of the south is lower than that of the north. The number of premature deaths attributed to ozone concentration during the warm season has decreased, but some southern cities such as Xinyang and Zhumadian have also seen an increase in premature deaths. China has achieved significant results in air pollution control, but in areas with high ozone concentrations and high population density, the health burden caused by air pollution remains heavy, and stricter air pollution control policies need to be implemented.

Deciphering multi-temporal scale dynamics in the concentration, sources and processes of near surface ozone over different climatic regions of India

This study aims to investigate the factors influencing seasonal and long-term (2003-2021) changes in the near surface ozone (850 hpa) concentrations over different climatic sub-regions of India. Detailed comparison of daily (2019-2021) near surface ozone values of ERA-5 and CAAQMS (Continuous Ambient Air Quality Monitoring Stations) ground-based measurements revealed that ERA-5 is temporally in phase with CAAQMS measurements falling indifferent climatic sub-regions of India. ERA-5 near surface ozone shows statistically significant long-term (2003-2021) positive trends [2-4 percent per decade (ppd)] over most of the climatic sub-regions, over Indo-Gangetic Planes (IGPs), Southern and Central India trends are particularly strong. Trends were also estimated for each season separately, which were largely positive (2-6 ppd) over Central and Southern India in the Autumn and Winter seasons. Extensive climatological analysis reveals that the reversal of winds in the Indian monsoonal system plays a vital role in such trend patterns across the Indian subcontinent. South-westerly winds from June through September presumably bring ozone deficit air of marine origin, thus causing a dilution effect while the North-easterly winds during late Autumn and early Winters plausibly bring ozone-rich air from the stratospheric-tropospheric efflux dominated Himalayan region. It allows near surface ozone enhancement over Central and Southern India. Seasonal Principal component analysis (PCA) revealed that precursor gases (CH4 and NO2) and climatic variables especially specific humidity (SH) are the primary drivers of near surface ozone variability in the Winter season, while in Spring, climatic variables like boundary layer height (BLH), temperature (T) and SH have a significant role. Principal component regression (PCR) reveals a long-term increase in near surface ozone levels mostly dominated by precursor concentration over IGPs and Southern sub-regions. Whereas, BLH, T and SH significantly explain near surface ozone trends over North-eastern and Coastal India.

Urban ozone variability using automated machine learning: inference from different feature importance schemes

Tropospheric ozone is an air pollutant at the ground level and a greenhouse gas which significantly contributes to the global warming. Strong anthropogenic emissions in and around urban environments enhance surface ozone pollution impacting the human health and vegetation adversely. However, observations are often scarce and the factors driving ozone variability remain uncertain in the developing regions of the world. In this regard, here, we conducted machine learning (ML) simulations of ozone variability and comprehensively examined the governing factors over a major urban environment (Ahmedabad) in western India. Ozone precursors (NO2, NO, CO, C5H8 and CH2O) from the CAMS (Copernicus Atmosphere Monitoring Service) reanalysis and meteorological parameters from the ERA5 (European Centre for Medium-Range Weather Forecast's (ECMWF) fifth-generation reanalysis) were included as features in the ML models. Automated ML (AutoML) fitted the deep learning model optimally and simulated the daily ozone with root mean square error (RMSE) of ~2 ppbv reproducing 84-88% of variability. The model performance achieved here is comparable to widely used ML models (RF-Random Forest and XGBoost-eXtreme Gradient Boosting). Explainability of the models is discussed through different schemes of feature importance, including SAGE (Shapley Additive Global importancE) and permutation importance. The leading features are found to be different from different feature importance schemes. We show that urban ozone could be simulated well (RMSE = 2.5 ppbv and R2 = 0.78) by considering first four leading features, from different schemes, which are consistent with ozone photochemistry. Our study underscores the need to conduct science-informed analysis of feature importance from multiple schemes to infer the roles of input variables in ozone variability. AutoML-based studies, exploiting potentials of long-term observations, can strongly complement the conventional chemistry-transport modelling and can also help in accurate simulation and forecast of urban ozone.

Reactive Chlorine Species Advancing the Atmospheric Oxidation Capacities of Inland Urban Environments

Chlorine (Cl) radicals from photolabile chlorine species are highly reactive and can affect the fate of air pollutants in the atmosphere. Although several campaigns have been conducted, typically in coastal environments, long-term observations of reactive chlorine species and their impacts on atmospheric oxidation capacities (AOCs) are lacking. Here, we report nearly full-year observations of Cl2 and ClNO2 levels in Beijing and evaluate their impacts on the AOC with a box model coupled with Cl chemistry. Cl radicals promote the circulation of OH-HO2-RO2 by accelerating the OH chain lengths by up to 12.6% on average, hence boosting the AOC, especially in the winter or spring. This promotion effect is nonlinearly dependent on the VOC and NOx concentrations, thus leading to a slight shift in ozone formation from a VOC-sensitive regime to a transition regime with seasonal differences. Given the ubiquitous reactive chlorines in polluted inland urban regions, the AOCs and the formation of secondary pollutants will be underestimated if the reactive chlorine species are neglected.

Approaches to investigate crop responses to ozone pollution: from O3 -FACE to satellite-enabled modeling

Ozone (O₃ ) is a damaging air pollutant to crops. As one of the most reactive oxidants known, O₃ rapidly forms other reactive oxygen species (ROS) once it enters leaves through stomata. Those ROS in turn can cause oxidative stress, reduce photosynthesis, accelerate senescence, and decrease crop yield. To improve and adapt our feed, fuel, and food supply to rising O₃ pollution, a number of Free Air Concentration Enrichment (O₃ -FACE) facilities have been developed around the world and have studied key staple crops. In this review, we provide an overview of the FACE facilities and highlight some of the lessons learned from the last two decades of research. We discuss the differences between C₃ and C₄ crop responses to elevated O₃ , the possible trade-off between productivity and protection, genetic variation in O₃ response within and across species, and how we might leverage this observed variation for crop improvement. We also highlight the need to improve understanding of the interaction between rising O₃ pollution and other aspects of climate change, notably drought. Finally, we propose the use of globally modeled O₃ data that are available at increasing spatial and temporal resolutions to expand upon the research conducted at the limited number of global O₃ -FACE facilities.