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.

Continental outflow of anthropogenic aerosols over Arabian Sea and Indian Ocean during wintertime: ICARB-2018 campaign.

Chemical characterisation of atmospheric aerosols over Arabian Sea (AS) and Indian Ocean (IO) have been carried out during the winter period (January to February 2018) as part of the Integrated Campaign for Aerosols, gases and Radiation Budget (ICARB-2018). Mass concentrations of organic carbon (OC), elemental carbon (EC), water soluble and insoluble OC (WSOC, WIOC), primary and secondary OC (POC, SOC), water-soluble inorganic ions and trace metals have been estimated with a view to identify and quantify the major anthropogenic pollutants affecting the oceanic environments. Aerosol mass loading was found to exhibit strong spatial heterogeneity (varying from 13 to 84 µg m-3), significantly modulated by the origin of air-mass trajectories. Chemical analysis of aerosols revealed the presence of an intense pollution plume over south-eastern coastal Arabian Sea, near to south-west Indian peninsula (extending from ~ 12°N to 0° at 75°E) with a strong latitudinal gradient (~3 µg m-3/deg. from north to south) dominated by anthropogenic species contributing as high as 73% (38% nss-SO42-, 24.2% carbonaceous aerosols (21% Organic Matter, 3.2% EC) and 10% NH4+). Anthropogenic signature over oceanic environment was also evident from the dominance and high enrichment of elements like Zn, Cu, Mn and Pb in trace metals. Long-range transport of air-masses originating from Indo Gangetic Plains and its outflow regions in Bay of Bengal, has been seen over Arabian Sea during winter, that imparted such strong anthropogenic signatures over this oceanic environment. Comparison with previous cruise studies conducted nearly two decades ago shows a more than two-fold increase in the concentration of nss-SO42-, over the continental outflow region in Arabian Sea.

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.

Decadal changes in surface ozone at the tropical station Thiruvananthapuram (8.542° N, 76.858° E), India: effects of anthropogenic activities and meteorological variability.

This paper presents the first observational results from an Indian station on the long-term changes in surface ozone (O3)-a major environmental pollutant and green house gas-over a period of about 40 years. It is based on the in situ measurements carried out during 1973-1975, 1983-1985, 1997-1998 and 2004-2014 at the tropical coastal station, Thiruvananthapuram. From 1973 to 1997, surface O3 shows a slow increase of ~ 0.1 ppb year-1 and a faster increase of 0.4 ppb year-1 afterwards till 2009 after which it showed a levelling off till 2012 followed by a minor decrease. The highest rate of increase is observed during 2005 to 2009 (2 ppb year-1), and the overall increase from 1973 to 2012 is ~ 10 ppb. The increase in day time O3 (peak O3) is estimated as 0.42 ppb year-1 during 1997-2012 and 2.93 ppb year-1 during 2006-2012. Interestingly, the long-term trend in O3 showed seasonal dependence which is more pronounced during O3 peaking seasons (winter/summer). The observed trends were analysed in the light of the changes in NO2, a major outcome of anthropogenic activities and methane which has both natural and anthropogenic sources and also meteorological parameters. Surface O3 and NO x exhibited positive association, but with varying rate of increase of O3 for NO x < 4 and > 4 ppb. Methane, a precursor of O3 also showed increase in tune with O3. Unlike many other high-latitude locations, meteorology plays a significant role in the long-term trends in O3 at this tropical site with water vapour abundance and strong solar irradiance which favour photochemistry. A comparison with the corresponding changes in the satellite-retrieved tropospheric column O3 (TCO) also showed an increase of 0.03 DU year-1 during 1996-2005 which enhanced to 0.12 DU year-1 after 2005. Both surface O3 and satellite-retrieved TCO were positively correlated with daily maximum temperature, increasing at the rate of 1.54 ppb °C-1 and 1.9 DU °C-1, respectively, on yearly basis. Surface O3 is found to be negatively correlated with water vapour content (ρv) at this tropical site, but at higher levels of ρv, O3 shows a positive trend.