Posts by Collection

Hyperlocal Air Quality Prediction using Machine Learning in East Bay Area, CA

In this project, I developed machine learning models to predict air quality on a city-block basis in Oakland and San Leandro, CA, without having to rely on complex physical modeling. I used a wide variety of publicly available datasets such as previously measured pollutant concentrations, local meteorological data, emissions from local industrial sources, and traffic information.

Check out my github repo and my blog post on this work!

Satellite Image Classification using Deep Learning

In this project, I used the DeepSat-6 satellite image dataset to build a multi-class classification algorithm using convolution neural networks to classify images based on the land use type.

Check out my github repo for more details!

US County-Level Air Pollution Sequestration Dataset

I developed a county-level spatial inventory of air pollutant sequestration by grassland and shrubland vegetation in the US. The dataset was developing using the daily leaf area index of vegetation classes, the National Land Cover dataset and the i-Tree Eco model. The dataset developed in this work is currently used by

1. California Department of Conservation and The Nature Conservancy in the TerraCount tool for land use planning.
2. U.S. Department of Forest Service in the i-Tree Eco tool
3. Bay Area GreenPrint Tool

Assessing the capacity of local ecosystems to meet industrial demand for ecosystem services

Published in AIChE Journal, 2016

Varsha Gopalakrishnan, Bhavik R. Bakshi, and Guy Ziv

Synergies and trade-offs in renewable energy landscapes: Balancing energy production with economics and ecosystem services

Published in Applied Energy, 2017

Authors: Rebecca J.Hanes, Varsha Gopalakrishnan, and Bhavik R. Bakshi.

Download here.

Biosolids management with net-zero CO2 emissions: a techno-ecological synergy design

Published in Clean Technologies and Environmental Policy, 2017

Authors: Varsha Gopalakrishnan, Geoffrey F. Grubb, and Bhavik R. Bakshi

Download here.

Including Nature in Engineering Decisions for Sustainability.

Published in Encyclopedia of Sustainable Technologies, 2017

Authors: Varsha Gopalakrishnan and Bhavik R. Bakshi

Download here.

Ecosystems as unit operations for local techno‐ecological synergy: Integrated process design with treatment wetlands

Published in AIChE Journal, 2018

Authors: Varsha Gopalakrishnan and Bhavik R. Bakshi

Download here.

Synergies between industry and nature–An emergy evaluation of a biodiesel production system integrated with ecological systems

Published in Ecosystems Services, 2018

Authors: Fabrizio Saladini, Varsha Gopalakrishnan, Simone Bastianoni, and Bhavik R. Bakshi

Download here.

Air quality and human health impacts of grasslands and shrublands in the United States

Published in Atmospheric Environment, 2018

Authors: Varsha Gopalakrishnan, Satoshi Hirabayashi, Guy Ziv, and Bhavik R. Bakshi.

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Including nature in the food-energy-water nexus can improve sustainability across multiple ecosystem services

Published in Resources, Conservation & Recycling, 2018

Authors: Rebecca J.Hanes, Varsha Gopalakrishnan, Bhavik R.Bakshi

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Role of vegetation in mitigating air emissions across industrial sites in the US

Published in ACS Sustainable Chemistry And Engineering, 2019

Authors: Varsha Gopalakrishnan, Bhavik R. Bakshi, and Guy Ziv

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Nature-Based Solutions Can Compete with Technology for Mitigating Air Emissions Across the United States

Published in Environmental Science & Technology, 2019

Authors: Varsha Gopalakrishnan, Guy Ziv, Satoshi Hirabayashi, and Bhavik R. Bakshi

Download here.

Using Machine Learning and Dispersion Modeling to Estimate Emissions

Published: January 19, 2020

More information here

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.