Trend and variability of atmospheric ozone over middle Indo-Gangetic Plain: impacts of seasonality and precursor gases

Impact of ozone pollution on crop yield, human health, and associated economic costs in the Indo-Gangetic plains

Ozone pollution is a growing problem in many developing countries posing challenges not only to air quality but also affecting agricultural productivity and human well-being. This is the first study in the Indo-Gangetic Plain exploring how the spatial variation and severity of tropospheric ozone affect both wheat yield and all-cause mortality. We estimated that ozone-related cumulative crop production loss for wheat in selected districts of IGP was 3.4 million tonnes during the study period (2019-2021), which amounted to 923 million USD. The production-weighted Relative Yield Loss (RYL) for wheat in the IGP was 9.3 % in 2019, 12.8 % in 2020, and 11.3 % in 2021. The losses incurred in 2021 could contribute to fulfilling the wheat requirements of 11.4 million people. We also assess the health and economic gains resulting from the attainment of the World Health Organization Air Quality Guidelines (WHO AQG) for ozone concentrations. It is estimated that interventions that achieve AQG would have averted 11,407 premature deaths in 2021 translating into an impressively large health and economic gain. The annual benefits in 2021 totaled to 34 billion USD. We observe that Uttar Pradesh experienced the highest losses, both in terms of crop damage and premature deaths. Our study observes that implementing policies to prepone the planting of wheat enhances food security by mitigating yield losses. Mitigating the health impact of ambient ozone necessitates a reduction in anthropogenic emissions and to attain this objective, we propose adopting an exposure-integrated source reduction approach.

Understanding the impact of elevated CO2 and O3 on growth and yield in Indian wheat cultivars: Implications for food security in a changing climate

The pressing issue of increasing tropospheric ozone (O3) levels necessitates the development of effective stress management strategies for plant protection. While considerable research has elucidated the adverse impacts of O3, understanding the combined effects of O3 and CO2 requires further investigation. This study focuses on assessing the response of stomatal O3 flux under various O3 and CO2 treatments, individually and in combination, and their repercussions on physiological, growth, and yield attributes in two Indian wheat cultivars, HUW-55 and PBW-550, which exhibit varying levels of sensitivities against elevated O3. Results indicated significant alterations in stomatal O3 flux in both O3-sensitive and tolerant wheat cultivars across different treatments, influencing the overall yield outcomes. Particularly, the ECO2+EO3 treatment demonstrated more positive yield protection in the O3-sensitive cultivar PBW-550, compared to HUW-55 indicating enhanced allocation of photosynthates towards reproductive development in PBW-550, compared to the tolerant cultivar HUW-55, as evidenced by higher harvest index (HI). Furthermore, the study revealed a stronger correlation between yield response and stomatal O3 flux in PBW-550 (R2 = 0.88) compared to HUW-55 (R2 = 0.79), as indicated by a steeper regression slope for PBW-550. The research also confirmed the role of elevated CO2 in reducing stomatal O3- flux in the tested cultivars, with discernible effects on their respective yield responses. Further experimentation is necessary to confirm these results across different cultivars exhibiting varying sensitivities to O3. These findings can potentially revolutionize agricultural productivity in regions affected by O3 stress. The criteria for recommending cultivars for agricultural practices should not be based only on their sensitivity/tolerance to O3. Still, they should also consider the effect of CO2 fertilization in the growing area. This experiment offers hope to sustain global food security, as the O3-sensitive wheat cultivar also showed promising results at elevated CO2. In essence, this research could pave the way for more resilient agricultural systems in the era of changing climate under elevated O3 and CO2 conditions.

High-spatial resolution ground-level ozone in Yunnan, China: A spatiotemporal estimation based on comparative analyses of machine learning models

Monitoring ground-level ozone concentrations is a critical aspect of atmospheric environmental studies. Given the existing limitations of satellite data products, especially the lack of ground-level ozone characterization, and the discontinuity of ground observations, there is a pressing need for high-precision models to simulate ground-level ozone to assess surface ozone pollution. In this study, we have compared several widely utilized ensemble learning and deep learning methods for ground-level ozone simulation. Furthermore, we have thoroughly contrasted the temporal and spatial generalization performances of the ensemble learning and deep learning models. The 3-Dimensional Convolutional Neural Network (3-D CNN) model has emerged as the optimal choice for evaluating the daily maximum 8-h average ozone in Yunnan Province. The model has good performance: a spatial resolution of 0.05° × 0.05° and strong predictive power, as indicated by a Coefficient of Determination (R2) of 0.83 and a Root Mean Square Error (RMSE) of 12.54 μg/m³ in sample-based 5-fold cross-validation (CV). In the final stage of our study, we applied the 3-D CNN model to generate a comprehensive daily maximum 8-h average ozone dataset for Yunnan Province for the year 2021. This application has furnished us with a crucial high-resolution and highly accurate dataset for further in-depth studies on the issue of ozone pollution in Yunnan Province.

Addressing ozone pollution to promote United Nations sustainable development goal 2: Ensuring global food security

Achieving global food security and ensuring sustainable agriculture, the dual objectives of the second Sustainable Development Goal (SDG 2), necessitate immediate and collaborative efforts from developing and developed nations. The adverse effects of ozone on crop yields have the potential to significantly undermine the United Nations' ambitious target of attaining food security and ending hunger by 2030. This review examines the causes of growing tropospheric ozone, especially in India and China which lead to a substantial reduction in crop yield and forest biomass. The findings show that a nexus of high population, rapid urbanization and regional pollution sources aggravates the problem in these countries. It elucidates that when plants are exposed to ozone, specific cellular pathways are triggered, resulting in changes in the expression of genes related to hormone production, antioxidant metabolism, respiration, and photosynthesis. Assessing the risks associated with ozone exposure involves using response functions that link exposure-based and flux-based measurements to variables like crop yield. Precisely quantifying the losses in yield and economic value in food crops due to current ozone levels is of utmost importance in comprehending the risks ozone poses to global food security. We conclude that policymakers should focus on implementing measures to decrease the emissions of ozone precursors, such as enhancing vehicle fuel efficiency standards and promoting the use of cleaner energy sources. Additionally, efforts should be directed toward mapping or developing crop varieties that can tolerate ozone, applying protective measures at critical stages of plant growth and establishing ozone-related vegetation protection standards.

A High-Performance Convolutional Neural Network for Ground-Level Ozone Estimation in Eastern China

Having a high-quality historical air pollutant dataset is critical for environmental and epidemiological research. In this study, a novel deep learning model based on convolutional neural network architecture was developed to estimate ground-level ozone concentrations across eastern China. A high-resolution maximum daily average 8-hour (MDA8) surface ground ozone concentration dataset was generated with the support of the total ozone column from the satellite Tropospheric Monitoring Instrument, meteorological data from the China Meteorological Administration Land Data Assimilation System, and simulations of the WRF-Chem model. The modeled results were compared with in situ measurements in five cities that were not involved in model training, and the mean R2 of predicted ozone with observed values was 0.9, indicating the good robustness of our model. In addition, we compared the model results with some widely used machine learning techniques (e.g., random forest) and recently published ozone datasets, showing that the accuracy of our model is higher and that the spatial distributions of predicted ozone are more coherent. This study provides an efficient and exact method to estimate ground-level ozone and offers a new perspective for modeling spatiotemporal air pollutants.

Climatological Study of Ozone over Saudi Arabia

In this work, analysis of the variability of total column ozone (TCO) over the Kingdom of Saudi Arabia (KSA) has been conducted during the 1979–2020 period based on the ECMWF-ERA5 dataset. It is found that the highest values of TCO appear in the spring and winter months especially over north KSA, while the lowest values of TCO occur in the autumn months. The highest values of the coefficient of variation (COV) for TCO occur in winter and spring as they gradually decrease southward, while the lowest COV values appear in summer and autumn. The Mann–Kendall test indicates that the positive trend values are dominant for the annual and seasonal TCO values over KSA, and they gradually increase southward. The study of long-term variability of annual TCO at KSA stations shows negative trend values are the dominant behavior during the 1979–2004 period, while positive trend values are the dominant behavior during the 2004–2020 period. The Mann–Whitney test assessed the abrupt change of the annual TCO time series at 28 stations in KSA and confirmed that there is an abrupt change towards increasing values around 2000, 2005, and 2014. The climatological monthly mean of the ozone mass mixing ratio (OMR) is studied at three stations representing the north, middle, and south of KSA. The highest values of OMR are found in the layer between 20 and 4 hPa with the maximum in summer and early autumn, while the lowest values are found below 100 hPa.

Regression-based flexible models for photochemical air pollutants in the national capital territory of megacity Delhi

Modelling photochemical pollutants, such as ground level ozone (O₃), nitric oxide (NO) and nitrogen dioxide (NO₂), in urban terrain was proven to be cardinal, chronophagous and complex. We built linear regression and random forest regression models using 4-years (2015-2018; hourly-averaged) observations for forecasting O₃, NO and NO₂ levels for two scenarios (1-month prediction (for January 2019) and 1-year prediction (for 2019)) - with and without the impact of meteorology. These flexible models have been developed for, both, localised (site-specific models) and combined (indicative of city-level) cases. Both models were aided with machine learning, to reduce their time-intensity compared to models built over high-performance computing. O₃ prediction performance of linear regression model at the city level, under both cases of meteorological consideration, was found to be significantly poor. However, the site-specific model with meteorology performed satisfactorily (r = 0.87; RK Puram site). Further, during testing, linear regression models (site-specific and combined) for NO and NO₂ with meteorology, show a slight improvement in their prediction accuracies when compared to the corresponding equivalent linear models without meteorology. Random forest regression with meteorology performed satisfactorily for indicative city-level NO (r = 0.90), NO₂ (r = 0.89) and O₃ (r = 0.85). In both regression techniques, increased uncertainty in modelling O₃ is attributed to it being a secondary pollutant, non-linear dependency on NO_x, VOCs, CO, radicals, and micro-climatic meteorological parameters. Analysis of importance among various precursors and meteorology have also been computed. The study holistically concludes that site-specific models with meteorology perform satisfactorily for both linear regression and random forest regression.

Surface ozone in the Doon Valley of the Himalayan foothills during spring

Elevated ozone (O₃) pollution is observed every spring over the Northern Indian region including the Himalayan foothills, with a maximum typically in the month of May. However, studies investigating influences of photochemistry and dynamics in the valleys of Central Himalaya are limited. Here, in situ surface O₃ observations conducted at Dehradun (77.99° E, 30.27° N, 600 m above mean sea level) in the Doon Valley during April-July 2018 are presented. These O₃ observations reveal the prevalence of an urban environment over Dehradun with enhanced levels during noontime (66.4 ppbv ± 11.0 ppbv in May) and lower levels during night (26.7 ppbv ± 11.5 ppbv). Morning time O₃ enhancement rate at Dehradun (7.5 ppbv h^-1) is found to be comparable to that at Bode (7.3 ppbv h^-1) in another valley of Himalayan foothills (Kathmandu), indicating stronger anthropogenic emissions in the Doon Valley as well. Daily average O₃ at Dehradun varied in the range of 13.7-71.3 ppbv with hourly values reaching up to 103.1 ppbv during the study period. Besides the in situ photochemical O₃ production, the entrainment of O₃-rich air through boundary layer dynamics also contributes in noontime O₃ enhancement in the Doon Valley. Monthly average O₃ at Dehradun (49.3 ppbv ± 19.9 ppbv) is observed to be significantly higher than that over urban sites in Northern India (35-41 ppbv) and Bode (38.5 ppbv) in the Kathmandu Valley during May. O₃ photochemical buildup, estimated to be 30.3 ppbv and 39.7 ppbv during April and May, respectively, is significantly lower in June (21.2 ppbv). Copernicus Atmosphere Monitoring Service (CAMS) model simulations successfully reproduce the observed variability in noontime O₃ at Dehradun (r = 0.86); however, absolute O₃ levels were typically overestimated. The positive relationship between CAMS O₃ and CO (r = 0.65) together with an O₃/CO slope of 0.16 is attributed to the influences of biomass burning besides anthropogenic emissions on observed O₃ variations in the Doon Valley. O₃ observations show an enhancement by 35-56% at Dehradun during a high-fire activity period in May 2018 as compared to a low-fire activity period over the Northern Indian region in agreement with the enhancement found in CAMS O₃ fields (10-65%) over the region in the vicinity of Dehradun.

Attributing mortality from temperature extremes: A time series analysis in Varanasi, India

Climate extremes are often associated with increased human mortality and such association varies considerably with space and time. We therefore, aimed to systematically investigate the effects of temperature extremes, daily means and diurnal temperature variations (DTV) on mortality in the city of Varanasi, India during 2009-2016. Time series data on daily mortality, air quality (SO₂, NO₂, O₃ and PM₁₀) and weather variables were obtained from the routinely collected secondary sources. A semiparametric quasi-Poisson regression model estimated the effects of temperature extremes on daily all-cause mortality adjusting nonlinear confounding effects of time trend, relative humidity and air pollution; stratified by seasons. An effect modification by age, gender and place of death as semi-economic indicator were also explored. Daily mean temperature was strongly associated with excess mortality, both during summer (5.61% with 95% CI: 4.69-6.53% per unit increase in mean temperature) and winter (1.53% with 95% CI: 0.88-2.18% per unit decrease in mean temperature). Daily mortality was found to be increased by 12.02% (with 95% CI: 4.21-19.84%) due to heat wave. The DTV has exhibited downward trend over the years and showed a negative association with all-cause mortality. Significant association of mortality and different metric of temperature extreme along with decreasing trend in DTV clearly indicate the potential impact of climate change on human health in the city of Varanasi. The finding may well be useful to prioritize the government policies to curb the factors that causes the climate change and for developing early warning system.

Estimation of personal PM2.5 and BC exposure by a modeling approach - Results of a panel study in Shanghai, China

BACKGROUND

Epidemiologic studies of PM_2.5 (particulate matter with aerodynamic diameter ≤2.5 μm) and black carbon (BC) typically use ambient measurements as exposure proxies given that individual measurement is infeasible among large populations. Failure to account for variation in exposure will bias epidemiologic study results. The ability of ambient measurement as a proxy of exposure in regions with heavy pollution is untested.

OBJECTIVE

We aimed to investigate effects of potential determinants and to estimate PM_2.5 and BC exposure by a modeling approach.

METHODS

We collected 417 24 h personal PM_2.5 and 130 72 h personal BC measurements from a panel of 36 nonsmoking college students in Shanghai, China. Each participant underwent 4 rounds of three consecutive 24-h sampling sessions through December 2014 to July 2015. We applied backwards regression to construct mixed effect models incorporating all accessible variables of ambient pollution, climate and time-location information for exposure prediction. All models were evaluated by marginal R² and root mean square error (RMSE) from a leave-one-out-cross-validation (LOOCV) and a 10-fold cross-validation (10-fold CV).

RESULTS

Personal PM_2.5 was 47.6% lower than ambient level, with mean (±Standard Deviation, SD) level of 39.9 (±32.1) μg/m³; whereas personal BC (6.1 (±2.8) μg/m³) was about one-fold higher than the corresponding ambient concentrations. Ambient levels were the most significant determinants of PM_2.5 and BC exposure. Meteorological and season indicators were also important predictors. Our final models predicted 75% of the variance in 24 h personal PM_2.5 and 72 h personal BC. LOOCV analysis showed an R² (RMSE) of 0.73 (0.40) for PM_2.5 and 0.66 (0.27) for BC. Ten-fold CV analysis showed a R² (RMSE) of 0.73 (0.41) for PM_2.5 and 0.68 (0.26) for BC.

CONCLUSION

We used readily accessible data and established intuitive models that can predict PM_2.5 and BC exposure. This modeling approach can be a feasible solution for PM exposure estimation in epidemiological studies.

Local and transboundary factors' impacts on trace gases and aerosol during haze episode in 2015 El Niño in Malaysia

Southeast Asian haze is a semi-natural phenomenon that chokes the region each year during the dry monsoon season. Smoke-haze episodes caused by the vegetation and peat fires in Indonesia severely affected large parts of Malaysia during the 2015 El Niño phenomenon. This study aimed to evaluate the factors that influenced the concentrations of aerosol and trace gases during the 2015 haze and non-haze period on a semi-urban site in the southern part of Malaysian peninsula that facing Sumatra (Muar, Site A), and on an urban site near to Kuala Lumpur, influenced by the city centre (Cheras, Site B). Local land use data and the cluster of air mass weighted backward trajectory were used to identify the potential factors from local sources and the transboundary region, respectively. The annual median concentrations of PM₁₀ for semi-urban and urban sites were 45.0μg/m³ and 47.0μg/m³, respectively for the study period (Jan-Dec 2015) from the hourly observation dataset. The highest PM₁₀ concentrations during the haze were 358μg/m³ and 415μg/m³ for the two sites, respectively, representing absolutely unhealthy air. However, the trace gases were within the safe threshold. The average concentrations of PM₁₀ and carbon monoxide were two fold higher during the haze than the non-haze episodes on both sites. Nitrogen dioxide was more influenced by haze compared with sulphur dioxide and ozone. The results of the land use change suggest that the local factor can also partially affect the air pollution on the urban area (Site B) but more visible in 2015. The results of the backward trajectory and the wildfire radiative power showed that the smoke-haze episodes that affected Malaysia in 2015 were mainly initiated in the Indonesian Sumatra and Kalimantan regions. This study provides a very useful information towards the impacted region during El Niño haze episode.

Airing 'clean air' in Clean India Mission

The submission explores the possibility of a policy revision for considering clean air quality in recently launched nationwide campaign, Clean India Mission (CIM). Despite of several efforts for improving availability of clean household energy and sanitation facilities, situation remain still depressing as almost half of global population lacks access to clean energy and proper sanitation. Globally, at least 2.5 billion people do not have access to basic sanitation facilities. There are also evidences of 7 million premature deaths by air pollution in year 2012. The situation is even more disastrous for India especially in rural areas. Although, India has reasonably progressed in developing sanitary facilities and disseminating clean fuel to its urban households, the situation in rural areas is still miserable and needs to be reviewed. Several policy interventions and campaigns were made to improve the scenario but outcomes were remarkably poor. Indian census revealed a mere 31% sanitation coverage (in 2011) compared to 22% in 2001 while 60% of population (700 million) still use solid biofuels and traditional cook stoves for household cooking. Further, last decade (2001-2011) witnessed the progress decelerating down with rural households without sanitation facilities increased by 8.3 million while minimum progress has been made in conversion of conventional to modern fuels. To revamp the sanitation coverage, an overambitious nationwide campaign CIM was initiated in 2014 and present submission explores the possibility of including 'clean air' considerations within it. The article draws evidence from literatures on scenarios of rural sanitation, energy practises, pollution induced mortality and climatic impacts of air pollution. This subsequently hypothesised with possible modification in available technologies, dissemination modes, financing and implementation for integration of CIM with 'clean air' so that access to both sanitation and clean household energy may be effectively addressed.