Performance evaluation of low cost air quality sensors (LCS)
Professor: Abhishek Chakraborty
UID: ES01
Environmental impacts of dietary choices in India
Professor: Srinidhi Balasubramanian
UID: ES02
ES01
Description: LCS is becoming popular for getting a high
spatio-temporally resolved air quality data. However, their
accuracy, reliability and data quality are often questioned,
which prevents their use for regulatory purposes. In this
project the LCS performance against other research and
regulatory grade equipment will b tested in various conditions
and some calibration model could be developed to improve LCS
data quality and performance.
Number of students: 2
Year of study: Students entering 3rd year, Students
entering 4th/5th year
CPI eligibility criteria: 7
Prerequisites: Some knowledge about air pollution, air
quality and programming skills to handle large dataset.
Duration: 4-6 months (open to discussion)
Learning outcome: 1. LCS types and functioning 2. Model
development 3. Field and lab work 4. Scientific publications
(possible)
Weekly time commitment: 15-20 hours
General expectations: Punctuality, dedication,
responsible behavior, frequent presence in lab
Assignment:
Instructions for assignment:
ES02
Description:Diets and more comprehensively food systems
depend on large inputs of energy and resources, and as a result,
are significant contributors to global environmental change.
Food production is linked to ~33% of the total greenhouse gas
emissions globally and is expected to increase in the future as
the population increases and lifestyles shift to increased
calories, animal-based foods, packaged and processed foods, and
food waste. Little is known about how Indian dietary choices
impact climate change. This study aims to quantify climate
impacts from Indian diets across regions and demographics using
a life-cycle approach using macro-scale health, nutritional and
geospatial data.
Number of students: 1
Year of study: Students entering 3rd year, Students
entering 4th/5th year
CPI eligibility criteria: 8
Prerequisites: Proficiency with Stata or similar
statistical software and working knowledge of Python preferred.
Student must be interested and available to spending 15-20 hours
at least each week for the work and must report to both the PhD
student driving the project and the PI.
Duration: 6-8 weeks
Learning outcome: "1. Understanding of life-cycle
analysis 2. Learning to use statistical and geospatial software
3. Immersion in learning technical communication and
professional etiquette "
Weekly time commitment:15-20
General expectations: Diligence, ability to frequently
and honestly communicate
Assignment:"1. Springmann, M., Clark, M., Mason-D’Croz,
D. et al. Options for keeping the food system within
environmental limits. Nature 562, 519–525 (2018).
https://doi.org/10.1038/s41586-018-0594-0 2. Fanzo, J. Healthy
and Sustainable Diets and Food Systems: the Key to Achieving
Sustainable Development Goal 2?. Food ethics 4, 159–174 (2019).
https://doi.org/10.1007/s41055-019-00052-6"
Instructions for assignment: "> 1-page précis on what the
student has learned and possible research directions for India
based on the following two papers. > Pay close attention to
grammar, formatting, and citations. > Students are welcome to
add additional relevant references to their precis. > Use of
ChatGPT and equivalent tools ""highly discouraged"". "