SCI Kit | Air Quality Tracking and Analysis

0 2219 Easy

Introduction:

Have you ever noticed how the air smells fresh and clean after it rains? 

Air quality is influenced by several factors, including weather conditions such as temperature, humidity, wind speed, precipitation and so on. In this experiment, we will set up a weather station equipped with a SCI DAQ module and sensors to monitor temperature, humidity, and air quality. The aim of this experiment is to determine the effects of weather conditions on air quality over a day.

Materials:

SCI DAQ Module x 1

SEN0514 Air Quality Sensor x 1

SEN0334 Temperature and Humidity Sensor x 1

4pin Wire x 2

Type-C Cable or Battery Holder

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Setup:

1. Power the SCI module from either battery or Type-C port. (Make sure the power source is reliable because the module will be recording data for a period of time.)

2. Connect the air quality sensor and temperature & humidity sensor to Port 2 and Port 3 respectively. The sensors will be automatically identified and their data will be displayed on the screen.

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Experiment:

Place the hardware in the desired location, for example, near a window, making sure that they are not exposed to rain to avoid damage. It may be beneficial to check the weather forecast and select a day with dramatic weather changes, such as a predicted rainfall or temperature drop, to observe significant changes in the collected data.

1. Set up the Experiment

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2. Record the Data

As we will be recording data over a relatively long period of time, and weather conditions may not change rapidly, setting a refresh rate of 5 minutes would be sufficient for this experiment to capture any significant changes in the data. To start recording data, simply press the R button.

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To conserve power, the screen can be turned off. Simply go to the Settings menu, find the Screen Off option, and press OK to disable the screen. To re-enable the screen and view the data display, press the OK button again.

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Data Analysis:

After the data has been recorded, it can be exported to a CSV file and plotted on a graph to visually identify trends or patterns. Comparing the air quality data with corresponding weather conditions can help establish correlations between the two, allowing for a better understanding of the impact of weather on air quality.

Conclusion:

Through this experiment, we set up a weather station to track data over a period of time and found that temperature and humidity can indeed have a significant impact on air quality. By continuing to study this connection, it is possible to create a model that will predict and prevent harmful pollution that can impact human health and help us take action to keep the air we breathe safe and healthy.

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