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.

Black carbon over tropical Indian coast during the COVID-19 lockdown: inconspicuous role of coastal meteorology.

Black carbon (BC) aerosols critically impact the climate and hydrological cycle. The impact of anthropogenic emissions and coastal meteorology on BC dynamics, however, remains unclear over tropical India, a globally identified hotspot. In this regard, we have performed in situ measurements of BC over a megacity (Chennai, 12° 59' 26.5″ N, 80° 13' 51.8″ E) on the eastern coast of India during January-June 2020, comprising the period of COVID-19-induced strict lockdown. Our measurements revealed an unprecedented reduction in BC concentration by an order of magnitude as reported by other studies for various other pollutants. This was despite having stronger precipitation during pre-lockdown and lesser precipitation washout during the lockdown. Our analyses, taking mesoscale dynamics into account, unravels stronger BC depletion in the continental air than marine air. Additionally, the BC source regime also shifted from a fossil-fuel dominance to a biomass burning dominance as a result of lockdown, indicating relative reduction in fossil fuel combustion. Considering the rarity of such a low concentration of BC in a tropical megacity environment, our observations and findings under near-natural or background levels of BC may be invaluable to validate model simulations dealing with BC dynamics and its climatic impacts in the Anthropocene.

Photochemical evolution of air in a tropical urban environment of India: A model-based study.

The photochemical processes over tropical Indian region impact the atmospheric composition and air quality over local to global scales; nevertheless, studies on detailed atmospheric chemistry remain sparse in this region. In this study, we investigate the photochemical evolution of air in the downwind of a tropical semi-arid urban environment (Ahmedabad) in India using the Master Mechanism model. The 5-days long chemical evolution has been simulated for the winter conditions - when this region experiences strong ozone build up. Model environment has been set up by including the meteorological conditions, overhead ozone, and aerosol loading, etc. Nitrogen oxides (NOx), carbon monoxide (CO), ozone (O3), and several volatile organic compounds (VOCs) have been initialized in the model based on the wintertime observations. The model predicts large O3 production (∼115 ppbv) in the downwind regions, followed by a gradual decrease from the 3rd day onwards. Additionally, significant amounts of the secondary inorganics, e.g. nitric acid (∼17 ppbv), hydrogen peroxide (∼9 ppbv), and organics, e.g. ketones (∼11 ppbv), are also simulated. The noontime maximum levels of hydroxyl (OH) and hydroperoxyl (HO2) radicals are simulated to be 0.3 and 44 pptv, respectively. While the production of OH is dominated by the reaction of NO with HO2 on the first day, photolysis of O3 dominates subsequently with reduction in NOx levels. VOCs are the major OH sink during day 1, however contribution of CO is greater on further days. The air mass trajectory analysis suggests the outflow of ozone-rich air over the rural areas and the Arabian Sea, in agreement with measurements and a global model. Our study highlights the strong impact of the urban outflows on the regional atmospheric composition. The continuous measurements of VOCs and radicals are needed over tropical regions to complement the models and further improve the understanding of air chemistry.

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

Purpose of Review: Fine particulate matter (PM2.5) and ground-level ozone (O3) pose a significant risk to human health. The World Health Organization (WHO) has recently revised healthy thresholds for both pollutants. The formation and evolution of PM2.5 and O3 are however governed by complex physical and multiphase chemical processes, and therefore, it is extremely challenging to mitigate both pollutants simultaneously. Here, we review mechanisms and discuss the science-informed pathways for effective and simultaneous mitigation of PM2.5 and O3. Recent Findings: Global warming has led to a general increase in biogenic emissions, which can enhance the formation of O3 and secondary organic aerosols. Reductions in anthropogenic emissions during the COVID-19 lockdown reduced PM2.5; however, O3 was enhanced in several polluted regions. This was attributed to more intense sunlight due to low aerosol loading and non-linear response of O3 to NO x . Such contrasting physical and chemical interactions hinder the formulation of a clear roadmap for clean air over such regions. Summary: Atmospheric chemistry including the role of biogenic emissions, aerosol-radiation interactions, boundary layer, and regional-scale transport are the key aspects that need to be carefully considered in the formulation of mitigation pathways. Therefore, a thorough understanding of the chemical effects of the emission reductions, changes in photolytic rates and boundary layer due to perturbation of solar radiation, and the effect of meteorological/seasonal changes are needed on a regional basis. Statistical emulators and machine learning approaches can aid the cumbersome process of multi-sector multi-species source attribution.

Frequency distribution of pollutant concentrations over Indian megacities impacted by the COVID-19 lockdown.

The megacities experience poor air quality frequently due to stronger anthropogenic emissions. India had one of the longest lockdowns in 2020 to curb the spread of COVID-19, leading to reductions in the emissions from anthropogenic activities. In this article, the frequency distributions of different pollutants have been analysed over two densely populated megacities: Delhi (28.70° N; 77.10° E) and Kolkata (22.57° N; 88.36° E). In Delhi, the percentage of days with PM2.5 levels exceeding the National Ambient Air Quality Standards (NAAQS) between 25 March and 17 June dropped from 98% in 2019 to 61% in 2020. The lockdown phase 1 brought down the PM10 (particulate matter having an aerodynamic diameter ≤ 10 µm) levels below the daily NAAQS limit over Delhi and Kolkata. However, PM10 exceeded the limit of 100 µgm-3 during phases 2-5 of lockdown over Delhi due to lower temperature, weaker winds, increased relative humidity and commencement of limited traffic movement. The PM2.5 levels exhibit a regressive trend in the highest range from the year 2019 to 2020 in Delhi. The daily mean value for PM2.5 concentrations dropped from 85-90 µgm-3 to 40-45 µgm-3 bin, whereas the PM10 levels witnessed a reduction from 160-180 µgm-3 to 100-120 µgm-3 bin due to the lockdown. Kolkata also experienced a shift in the peak of PM10 distribution from 80-100 µgm-3 in 2019 to 20-40 µgm-3 during the lockdown. The PM2.5 levels in peak frequency distribution were recorded in the 35-40 µgm-3 bin in 2019 which dropped to 15-20 µgm-3 in 2020. In line with particulate matter, other primary gaseous pollutants (NOx, CO, SO2, NH3) also showed decline. However, changes in O3 showed mixed trends with enhancements in some of the phases and reductions in other phases. In contrast to daily mean O3, 8-h maximum O3 showed a reduction over Delhi during lockdown phases except for phase 3. Interestingly, the time of daily maximum was observed to be delayed by ~ 2 h over Delhi (from 1300 to 1500 h) and ~ 1 h over Kolkata (from 1300 to 1400 h) almost coinciding with the time of maximum temperature, highlighting the role of meteorology versus precursors. Emission reductions weakened the chemical sink of O3 leading to enhancement (120%; 11 ppbv) in night-time O3 over Delhi during phases 1-3.

Exploring the potential of machine learning for simulations of urban ozone variability.

Machine learning (ML) has emerged as a powerful technique in the Earth system science, nevertheless, its potential to model complex atmospheric chemistry remains largely unexplored. Here, we applied ML to simulate the variability in urban ozone (O3) over Doon valley of the Himalaya. The ML model, trained with past variations in O3 and meteorological conditions, successfully reproduced the independent O3 data (r2 ~ 0.7). Model performance is found to be similar when the variation in major precursors (CO and NOx) were included in the model, instead of the meteorology. Further the inclusion of both precursors and meteorology improved the performance significantly (r2 = 0.86) and the model could also capture the outliers, which are crucial for air quality assessments. We suggest that in absence of high-resolution measurements, ML modeling has profound implications for unraveling the feedback between pollution and meteorology in the fragile Himalayan ecosystem.

Bioaerosol impact on crop health over India due to emerging fungal diseases (EFDs): an important missing link.

Atmospheric bioaerosols, which contain a diverse group of various biological materials, also include pathogenic microorganisms such as viruses, bacteria, and fungal spores. The dispersal of various pathogens negatively impacts the human and ecosystem health. While the impact of pathogenic bacteria and viruses on human and ecosystem health is well documented, the impact of fungal spores on crop, however, is poorly characterized. An unprecedented increase in number of fungal and fungal-like diseases (emerging fungal diseases (EFDs)) in plants is threatening the food security and endangering the biodiversity. In present communication, we show an increasing trend in the fungal bioaerosol attacks on crops over India outstripping bacteria and viruses. We further argue about the complex interactions between the fungal species, and crop impact over India is unique and highly interconnected with the topography, meteorological variables, and season of the year. Under constantly warming scenario, the fungal attacks on plants are expected to rise and, in all likelihood, extend to the sensitive and fragile ecosystems like the Himalayan region and the Western Ghats. An increasing trend in EFDs calls for immediate coordinated efforts towards understanding the type and diversity of pathogenic fungal bioaerosols. There is, however, a lack over Indian region about biogeography of pathogenic fungi. The detailed biogeography would help in improving public and political awareness to formulate the effective policy decisions. Any further disregard and delay in recognizing the importance of EFDs to crop and sensitive ecosystems can have severe societal and ecological repercussions over Indian region.

On the widespread enhancement in fine particulate matter across the Indo-Gangetic Plain towards winter.

Fine particulate matter (PM2.5, aerodynamic diameter ≤2.5 µm) impacts the climate, reduces visibility and severely influences human health. The Indo-Gangetic Plain (IGP), home to about one-seventh of the world's total population and a hotspot of aerosol loading, observes strong enhancements in the PM2.5 concentrations towards winter. We performed high-resolution (12 km × 12 km) atmospheric chemical transport modeling (WRF-Chem) for the post-monsoon to winter transition to unravel the underlying dynamics and influences of regional emissions over the region. Model, capturing the observed variations to an extent, reveals that the spatial distribution of PM2.5 having patches of enhanced concentrations (≥100 µgm-3) during post-monsoon, evolves dramatically into a widespread enhancement across the IGP region during winter. A sensitivity simulation, supported by satellite observations of fires, shows that biomass-burning emissions over the northwest IGP play a crucial role during post-monsoon. Whereas, in contrast, towards winter, a large-scale decline in the air temperature, significantly shallower atmospheric boundary layer, and weaker winds lead to stagnant conditions (ventilation coefficient lower by a factor of ~4) thereby confining the anthropogenic influences closer to the surface. Such changes in the controlling processes from post-monsoon to winter transition profoundly affect the composition of the fine aerosols over the IGP region. The study highlights the need to critically consider the distinct meteorological processes of west-to-east IGP and changes in dominant sources from post-monsoon to winter in the formulation of future pollution mitigation policies.

Distribution of volatile organic compounds over Indian subcontinent during winter: WRF-chem simulation versus observations.

We investigate the distribution of volatile organic compounds (VOCs) over Indian subcontinent during a winter month of January 2011 combining the regional model WRF-Chem (Weather Research and Forecasting model coupled with Chemistry) with ground- and space-based observations and chemical reanalysis. WRF-Chem simulated VOCs are found to be comparable with ground-based observations over contrasting environments of the Indian subcontinent. WRF-Chem results reveal the elevated levels of VOCs (e. g. propane) over the Indo-Gangetic Plain (16 ppbv), followed by the Northeast region (9.1 ppbv) in comparison with other parts of the Indian subcontinent (1.3-8.2 ppbv). Higher relative abundances of propane (27-31%) and ethane (13-17%) are simulated across the Indian subcontinent. WRF-Chem simulated formaldehyde and glyoxal show the western coast, Eastern India and the Indo-Gangetic Plain as the regional hotspots, in a qualitative agreement with the MACC (Monitoring Atmospheric Composition and Climate) reanalysis and satellite-based observations. Lower values of RGF (ratio of glyoxal to formaldehyde <0.04) suggest dominant influences of the anthropogenic emissions on the distribution of VOCs over Indian subcontinent, except the northeastern region where higher RGF (∼0.06) indicates the role of biogenic emissions, in addition to anthropogenic emissions. Analysis of HCHO/NO2 ratio shows a NOx-limited ozone production over India, with a NOx-to-VOC transition regime over central India and IGP. The study highlights a need to initiate in situ observations of VOCs over regional hotspots (Northeast, Central India, and the western coast) based on WRF-Chem results, where different satellite-based observations differ significantly.

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.