Faculty Publications
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Item Spatiotemporal Analysis(Springer Science and Business Media B.V., 2020) Bhattacharjee, S.; Madl, J.; Chen, J.; Kshirsagar, V.Several aspects of spatiotemporal analysis of trace gases have been discussed, including visualization, validation, and different spatiotemporal analysis methods, such as missing data handling, atmospheric transport modeling, inverse modeling, machine learning methods, etc. Each one of them explores the characteristics of atmospheric trace gases like CO2,CH4, NO2, etc., in different application domains and help to under-stand the global and local atmospheric processes worldwide. Satellite-borne trace gas data, combined with various ground-based monitoring networks, are the foundation that enables a broad spectrum of their spatiotemporal analysis. Different investigations around the globe have been mentioned here in order to show traditional methods for the spatiotemporal analysis of trace gases and investigate the recent extensions created with data fusion approaches in the future. Though the discussion is not exhaustive, it gives the initial pointers for further exploration. © Springer Nature Switzerland AG 2022.Item Spatiotemporal Modeling(Springer Science and Business Media B.V., 2020) Bhattacharjee, S.; Madl, J.; Chen, J.; Kshirsagar, V.Spatiotemporal dataset has three components in general, attributes, space, and time. Modeling approaches of spatiotemporal data have covered a broad spectrum of applications in many fields, including environmental applications, crime hotspot analysis, healthcare informatics, transportation modeling, social media, and many others. Clustering, predictive learning, frequent pattern mining, anomaly detection, change detection, and relationship mining are the few broad categories of the modeling approaches irrespective of the applications (Atluri et al. 2018). This chapter discusses some modeling approaches used for environmental applications in general. Further, one spatiotemporal modeling approach of outlier detection is chosen and presented here. Outlier detection within the application data is an essential preprocessing step for most of the spatiotemporal applications. Some important literature on spatiotemporal outlier detection methods is also discussed. Though the applications and methods presented here are not exhaustive, this chapter gives the initial pointers for further exploration of the spatiotemporal models. © Springer Nature Switzerland AG 2022.Item Tropospheric NO2 and O3 Response to COVID-19 Lockdown Restrictions at the National and Urban Scales in Germany(John Wiley and Sons Inc, 2021) Balamurugan, V.; Chen, J.; Qu, Z.; Bi, X.; Gensheimer, J.; Shekhar, A.; Bhattacharjee, S.; Keutsch, F.N.This study estimates the influence of anthropogenic emission reductions on nitrogen dioxide ((Formula presented.)) and ozone ((Formula presented.)) concentration changes in Germany during the COVID-19 pandemic period using in-situ surface and Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI) satellite column measurements and GEOS-Chem model simulations. We show that reductions in anthropogenic emissions in eight German metropolitan areas reduced mean in-situ (& column) (Formula presented.) concentrations by 23 (Formula presented.) (& 16 (Formula presented.)) between March 21 and June 30, 2020 after accounting for meteorology, whereas the corresponding mean in-situ (Formula presented.) concentration increased by 4 (Formula presented.) between March 21 and May 31, 2020, and decreased by 3 (Formula presented.) in June 2020, compared to 2019. In the winter and spring, the degree of (Formula presented.) saturation of ozone production is stronger than in the summer. This implies that future reductions in (Formula presented.) emissions in these metropolitan areas are likely to increase ozone pollution during winter and spring if appropriate mitigation measures are not implemented. TROPOMI (Formula presented.) concentrations decreased nationwide during the stricter lockdown period after accounting for meteorology with the exception of North-West Germany which can be attributed to enhanced (Formula presented.) emissions from agricultural soils. © 2021. The Authors.Item Downscaled XCO2 Estimation Using Data Fusion and AI-Based Spatio-Temporal Models(Institute of Electrical and Electronics Engineers Inc., 2024) Pais, S.M.; Bhattacharjee, S.; Anand Kumar, M.; Chen, J.One of the well-known greenhouse gases (GHGs) produced by anthropogenic human activity is carbon dioxide (CO2). Understanding the carbon cycle and how negatively it affects the ecosystem requires analysis of the rise in CO2 concentration. This work aims to map CO2 concentration for the entire surface, making it useful for regional carbon cycle analysis. Here, column-averaged CO2 dry mole fraction, called XCO2, measured by the orbiting carbon observatory-2 (OCO-2) satellite, is used. Because of spectral interference by the clouds and aerosols, there are many missing footprints in the Level-2 swath of OCO-2, making it disruptive to understand any assessment related to the carbon cycle. The objective of this work is to predict 1 km2 XCO2 using data resampling and machine learning models. This work achieves a minimum mean absolute error (MAE) and root mean square error (RMSE) of 0.3990 and 0.8090 ppm, using the monthly models. © 2004-2012 IEEE.Item High-Spatial-Resolution Estimation of XCO2 Using a Stacked Ensemble Model(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Pais, S.M.; Bhattacharjee, S.; Anand Kumar, M.; Balamurugan, V.; Chen, J.Highlights: What are the main findings? The study develops a customized stacked ensemble model that generalizes (Formula presented.) predictions across multiple country, such as Germany, France, and Japan. It produces gap-filled high-resolution monthly, seasonal, and yearly maps, highlighting vegetation dynamics and seasonal cycles. What is the implication of the main finding? The customized stacked ensemble model provides reliable cross-country (Formula presented.) predictions at 1 (Formula presented.) resolution, validated against TCCON and CAMS, supporting large-scale environmental monitoring. Seasonal and yearly analyses show vegetation dynamics and photosynthetic activity significantly influence (Formula presented.), enhancing the model’s adaptability for agriculture, different climate assessments, and future global mapping. One of the leading causes of climate change and global warming is the rise in carbon dioxide ((Formula presented.)) levels. For a precise assessment of (Formula presented.) ’s impact on the climate and the creation of successful mitigation methods, it is essential to comprehend its distribution by analyzing (Formula presented.) sources and sinks, which is a challenging task using sparsely available ground monitoring stations and airborne platforms. Therefore, the data retrieved by the Orbiting Carbon Observatory-2 (OCO-2) satellite can be useful due to its extensive spatial and temporal coverage. Sparse and missed retrievals in the satellite make it challenging to perform a thorough analysis. This work trains machine learning models using the Orbiting Carbon Observatory-2 (OCO-2) (Formula presented.) retrievals and auxiliary features to obtain a monthly, high-spatial-resolution, gap-filled (Formula presented.) concentration distribution. It uses a multi-source aggregated (MSD) dataset and the generalized stacked ensemble model to predict country-level high-resolution (1 (Formula presented.)) (Formula presented.). When evaluated with TCCON, this country-level model can achieve an RMSE of 1.42 ppm, a MAE of 0.84 ppm, and (Formula presented.) of 0.90. © 2025 by the authors.