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Observed Compound Heat–particulate Pollution Events in an Amazonian city during the Extreme 2023 Drought: Evidence from a Low-cost Monitoring Network

Observed Compound Heat–particulate Pollution Events is a M.Tech project topic for Environmental Engineering. Explore the IEEE-style abstract, reference…

Observed Compound Heat–particulate Pollution Events is a M.Tech project topic for Environmental Engineering. It gives students a clear starting point for research, implementation planning, and documentation.

Observed Compound Heat–particulate Pollution Events Project Details

Abstract

This project looks at how climate‑driven heatwaves and poor air quality combine in tropical cities, with a focus on Santarém in the Amazon during the severe 2023 drought. We used continuous data from a local low‑cost sensor network and regional weather records. The goal was to see how particulate matter (PM2.5 and PM10) and extreme heat events occur together and interact. Heatwaves were defined with the CTX90pct index, which comes from long‑term records of daily maximum temperature. We applied statistical analysis, categorical classification, and multiple linear regression to link weather variables with particle concentrations. The results show that both fine (PM2.5) and coarse (PM10) particles rise sharply during heatwaves, especially

when the drought is intense and regional biomass burning is happening. The regression model points to ambient air temperature as the main factor driving particle levels, while higher relative humidity helps reduce them. Overall, the study offers a clear method for setting up low‑cost sensor networks to track combined environmental risks. It provides a useful framework for city air‑quality management and climate‑adaptation planning in vulnerable tropical regions.

Reference Paper Observed Compound Heat–particulate Pollution Events in an Amazonian city during the Extreme 2023 Drought: Evidence from a Low-cost Monitoring Network
Domain Environmental Engineering
Sub-Domain Pollution Control / Air Quality
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