In: Other
Hypothesis: If the temperature increases, then the air pollution will get worse.
Describe the procedures needed to test this hypothesis and what equipment would be needed.
The hypothesis that the air pollution will get worse with increasing temperature, need certain things to be defined beforehand. The first and foremost thing is what you will definitely as pollution. For instance it might be the concentration of particulate matter at 2.5 micrometer or 10 micrometer, the concentration of Troposphere ozone, concentration of NO2, Carbon monoxide etc. After defining your definition of pollution you need proper equipment to make measurements in scientific terms. Thus you will need thermometer, particulate and gas measurement devices etc. If you have access to data of such measurements of variables of your interest, from a reliance source such as government body you can directly do the statistical experiment without making firsthand measurements.
When you have the data measurements either equipment measurements or secondary data, you can analyse statistically to test your hypothesis.
The most important statistical instrument for this can be a Correlation Coefficient analysis. You can use the temperature measurements of different times or areas and pollution measurements as two variables, which you will analyse through correlational analysis.
After doing the statistical test if you get a positive correlation coefficient value( which ranges between -1 and +1) ,it means there is positive correlation between pollution measurements and temperature measurements, i.e. both rise or fall together. Means if temperature go up ,so do pollution measurements and as pollution levels go up, so do temperature measurements. So this means you will maintain your hypothesis that if the temperature increases air pollution will get worse.
On the other hand if the correlation value is negative it means increase in temperature will result in decline in pollution levels, so you will rejected your hypothesis.
So in this way you can easily test your hypothesis.