Month: April 2024

Hacking the Herd: How Social Norms Can Inspire You (and Everyone Else) to Change

Sotirios Papathanasiou2 Comments

Social norms are like unwritten rules that a group of people follow. They’re the guidelines for how we behave in certain situations, and they help keep things running smoothly. Social norms are still a powerful tool for inspiring behavior change for a few reasons:

  • Leveraging the Desire to Belong: People are social creatures with a natural desire to fit in with their groups. Social norms highlight what behaviors are expected and accepted, nudging people to conform to avoid social disapproval.
  • Focus on Prevalence: Social norms campaigns can emphasize that the desired behavior is actually more common than people think. This can counteract the feeling of being alone in adopting a new behavior.
  • Positive Reinforcement: Seeing others engage in the positive behavior can provide encouragement and a sense of community around the change.

Here’s how social norms can be applied to air pollution:

  • Highlighting Eco-Friendly Choices: Campaigns can showcase people using public transport, carpooling, or opting for sustainable products. This reframes these actions as the norm, making them more likely to be adopted by others.
  • Community Recognition: Programs that recognize individuals or businesses for their efforts to reduce air pollution can create positive social pressure and inspire others to follow suit.
  • Countering Misconceptions: Social norms campaigns can address the misconception that individual actions don’t make a difference. By highlighting the collective impact of many small changes, they can motivate people to take action.

For example, a campaign might feature a slogan like “Most people in our community use heat pumps – Join the Movement for Cleaner Air!” This approach uses social norms (descriptive norm – what people actually do) to encourage alternative heating to wood burning stoves (desired behavior).

By framing eco-friendly behaviors as the social norm, communities can create a more sustainable environment and improve air quality for everyone.

New Silica Dust Limits Aim to Better Protect Miners’ Health

Sotirios PapathanasiouLeave a comment

On April 18th, 2024, the US Department of Labor has issued a final rule that reduces the permissible exposure limit (PEL) for crystalline silica in mines. Silica dust is a known health hazard that can cause silicosis, a debilitating lung disease. The new rule is intended to better protect miners from irreversible workplace illnesses, it will take effect on June 17th, 2024.

Health Risks of Silica Dust

Silica dust is a component of sand, rock, and quartz. When miners inhale silica dust, it can scar the lungs, leading to silicosis, which reduces their ability to take in oxygen. Silicosis can cause shortness of breath, coughing, and chest pain. In severe cases, it can be fatal.

New Exposure Limits

The final rule reduces the PEL for silica dust to to 50 μg/m3 of air for a full-shift exposure, calculated as an 8-hour time-weighted average. This is a significant reduction that will help to reduce miners’ risk of developing silicosis. The action level, or the amount warranting remedial action, is 25 μg/m3 of air. 

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MasterClass: Air Quality Data Visualization with R Studio & Packages

Sotirios Papathanasiou2 Comments

R Studio and its packages are used by hundreds of thousands of people to make millions of plots. I use it to compare air sensor data from different air quality monitors/sensors or to visualize air pollution levels.

In this article we will explore both how we can visualize air quality data from publicly available sources and how you can create statistical correlations between different pollutants or different sensors to find the correlation coefficient or correlation of determination.

First: Get the Right Packages

Packages are collections of functions, data, and compiled code in a well-defined format, created to add specific functionality. Here are some of the packages that we will install inside RStudio and use.

#You can either get ggplot2 by installing the whole tidyverse library
install.packages(tidyverse)

#Alternatively, install just ggplot2
install.packages(ggplot2)

#saqgetr is a package to import European air quality monitoring data in a fast and easy way
install.packages(saqgetr)

#worldmet provides an easy way to access data from the NOAA Integrated Surface Database
install.packages(worldmet)

#Date-time data can be frustrating to work with in R and lubridate can help us fix possible issues
install.packages(lubridate)

#Openair is a package developed for the purpose of analysing air quality data
linstall.packages(openair)
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From Boom to Bust: The Great IoT Air Quality Recession

Sotirios Papathanasiou2 Comments

The once booming Internet of Things (IoT) air quality monitoring market is facing a harsh reality check. Fueled by a surge in AI startups attracting investments and a subsequent saturation of low-cost air quality monitors, the industry is experiencing a period of upheaval. This downturn, dubbed “The Great IoT Air Quality Recession,” is forcing companies to adapt or face extinction. I see many high-profile executives leaving previously thought innovative startups in the realm of air quality in search of a more “stable” future.

A Wave of Investment and Sensor Saturation

AI startups like ChatGTP and similar, promising to leverage the power of machine learning to generate content or analyze data, became investor darlings. This new influx of cash is fueling the decline of IoT low-cost air quality solutions.

After the COVID-19 pandemic, the market quickly became saturated with low-cost monitors that promised that will fix indoor and outdoor environments. Buildings were filled with cheap monitors, but actionable insights remained scarce. The promised AI-powered analysis, in many cases, failed to materialize. Consumers were left with a plethora of data points with no clear understanding of what it all meant or what to do.

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