Kousik Das

About

I am a geographer specializing in spatial modelling, machine learning, and urban climate, with a Ph.D. in Geography from Vidyasagar University. My research focuses on understanding urban air pollution dynamics and their linkages with land-use patterns, climate variability, and public health.

I work at the intersection of GIS, remote sensing, and data-driven modelling, applying tools such as Google Earth Engine, ArcGIS, and R. My experience includes developing Generalized Linear Models (GLM), Land-Use Regression (LUR), and advanced machine learning models (Random Forest, SVM, XGBoost) to analyze PM₂.₅ and PM₁₀ variability and predict environmental risks.

I have also conducted pollutant source apportionment using HYSPLIT back-trajectory and PSCF analysis, and assessed human exposure through respiratory deposition dose (RDD) modelling using high-resolution sensor data. My work integrates environmental science with public health to provide actionable insights for sustainable urban planning.

I am passionate about contributing to interdisciplinary research that addresses real-world environmental challenges and supports climate-resilient and healthy cities, particularly in rapidly urbanizing regions.

Employment

• Sadhu Ramchand Murmu University of Jhargram Resource Person for Postgraduate Teaching Programs
  2026 - Present

Education

• Vidyasagar University Doctor of Philosophy (PhD)
  2018 - 2026
• Vidyasagar University Master Degree
  2013 - 2015

Projects & Funding

Projects & funding information is unavailable.

Publications (18)

• Estimating the Variability of Ground-Level Annual PM2.5 and PM10 Using Land-Use Regression Model in Kolkata Municipal Corporation (KMC)
  2023 DOI: 10.1007/978-3-031-31399-8_17
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• Application of land-use regression model with regularization algorithm to assess PM2.5 and PM10 concentration and health risk in Kolkata Metropolitan
  Urban Climate 2023 DOI: 10.1016/j.uclim.2023.101473
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• Channel instability and hydrogeomorphic adjustment in alluvial reach of Kangsabati River, India using Digital Shoreline Analysis System and Acoustic Doppler Current Profiler
  Geocarto International 2022 DOI: 10.1080/10106049.2022.2107712
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• Multifunctional resilience of river health to human service demand in an alluvial quarried reach: a comparison amongst fuzzy logic, entropy, and AHP-based MCDM models
  Environmental Science and Pollution Research 2022 DOI: 10.1007/s11356-022-21040-0
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• Analysis of eve-teasing potential zones using geospatial technologies and AHP: a study in Midnapore town, West Bengal, India
  GeoJournal 2021 DOI: 10.1007/s10708-019-10106-1
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• Morphometric analysis to characterize the soil erosion susceptibility in the western part of lower Gangetic River basin, India
  Arabian Journal of Geosciences 2021 DOI: 10.1007/s12517-021-06819-8
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• Land use and Land Cover change and its resultant erosion susceptible level: an appraisal using RUSLE and Logistic Regression in a tropical plateau basin of West Bengal, India
  Environment, Development and Sustainability 2021 DOI: 10.1007/s10668-020-00628-x
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• Examine the impact of weather and ambient air pollutant parameters on daily case of COVID-19 in India
  2020 DOI: 10.1101/2020.06.08.20125401
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• Estimation of Erosion Susceptibility and Sediment Yield in Ephemeral Channel Using RUSLE and SDR Model: Tropical Plateau Fringe Region, India
  Gully Erosion Studies from India and Surrounding Regions 2020 DOI: 10.1007/978-3-030-23243-6_10
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• Sub-basin prioritization for assessment of soil erosion susceptibility in Kangsabati, a plateau basin: A comparison between MCDM and SWAT models
  Science of The Total Environment 2020 DOI: 10.1016/j.scitotenv.2020.139474
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