AIR QUALITY INDEX
The Air Quality Index (AQI) is used for reporting daily air quality. It tells us how clean or polluted air is.
The AQI is calculated based on the average concentration of a particular pollutant measured over a standard time interval (24 hours for most pollutants, 8 hours for carbon monoxide and ozone).
For example, the AQI for PM2.5 is based on 24-hour average concentration
How AQI is influenced by Climatic Conditions.
Climate change influences air as ambient air pollutants are very sensitive to meteorological conditions.
Air quality is strongly dependent on weather and is therefore sensitive to climate change.
Recent studies have provided estimates of this climate effect through correlations of air quality with meteorological variables.
How is air pollution affected by weather depends-- some types of pollution are worse in the summer heat, while others are worse in cold winter weather.
The same atmospheric conditions that create weather-- air pressure, temperature, and humidity-- also affect air quality.
Because of climate warming, the Earth experiences more extreme weather, such as heat waves and drought, which can negatively Impact air quality.
Purpose/Idea
The Purpose of this project is to use Meteorological variables for past years data for a and use them to predict the Air Quality Index of a particular pollutant of a Particular region.
Data Sources
To predict the air quality index of a particular region , we need the
pollutant concentration of a particular pollutant (PM 2.5 AQI) which will be available in the cpcb.nic.in website,
Historical Metereological Data which will be collected from website en.tutiempo.net/
Techniques
Feature engineering
Supervised Learning Algorithms (Linear Regression Lasso and Ridge Regression Decision Tree Regressor KNN Regressor Random Forest Regressor)
Targets
BY MIDWAY Collection of Data, Preprocess the Data Apply Different Supervised algorithms and Identify which works best
AFTER MIDWAY Suggest possible improvements Report Writing
Expected Results
The model should be capable for successfully predicting the air quality index of a total county or any state or any bounded region provided with the Metereological Data
Relevant papers
Effect of climate change on air quality Daniel J. Jacob a,*, Darrell A. Winner b a School of Engineering and Applied Science, Harvard University, Cambridge, MA, USA bOffice of Research & Development, U.S. Environmental Protection Agency, Washington, DC, USA
link
Climate Change is Threatening Air Quality across the Country
Published: July 30th, 2019
Research brief by Climate Central
link
RESEARCH ARTICLE Impacts of climate change on future air quality and human health in China
Analysis of related papers..
Effect of climate change on air quality Daniel J. Jacob a,*, Darrell A. Winner b a School of Engineering and Applied Science, Harvard University, Cambridge, MA, USA bOffice of Research & Development, U.S. Environmental Protection Agency, Washington, DC, USA
link
It reviewed current knowledge of the effect of climate change on air quality with focus on 21st-century projections for ozone and particulate matter (PM).It studied observed correlations of ozone and PM with meteorological variables. The two air pollutants of most concern for public health are surface ozone and particulate matter, and they are the focus of the review. It was observed that correlations of PM concentrations with meteorological variables are weaker than for ozone
Climate Change is Threatening Air Quality across the Country
Published: July 30th, 2019
Research brief by Climate Central
https://www.climatecentral.org/news/climate-change-is-threatening-air-quality-across-the-country-2019
Climate Central analyzed summer high temperatures and used data from the NOAA/NCEI Air Stagnation Index, which incorporates upper atmospheric winds, surface winds, and precipitation to identify the number of stagnant days in a month.
The same atmospheric conditions that create weather-- air pressure, temperature, and humidity-- also affect air quality.
The analysis showed a positive correlation between summer maximum temperatures and the number of summer stagnant days.
As the climate warms, stagnant days are projected to increase further, with up to 40 more days per year by late-century.
And stagnation impacts air quality. When the air is stagnant, pollutants react together in the heat and sun, and high concentrations of ground-level ozone can build up.
RESEARCH ARTICLE
Impacts of climate change on future air quality and human health in China
This paper presents correlation analyses between changes in ozone and PM2.5 concentrations and these meteorological variables. that the increase in ozone concentrations is generally associated with the increase in temperature and the decrease in precipitation, while the increase in PM2.5 concentrations is generally associated with the decrease in boundary layer height and wind speed.
Creating Dataset
The Pollutant data was available from 2018 onwards and downloaded from cpcb website
The meteorological variables were webscrapped from https://en.tutiempo.net/
To collect the meteorological variables data Html file of website was webscrapped .
Then Using beautiful soup html data was written into a csv Format
Data was cleaned (removing null values, removing empty rows)
And AQI data was combined to form final dataset.
Visualising Data
Dataset was first imported , and checked for null values in Dataframe, and remove the null values.
Divide dataset into dependent and independent features
Pairwise relationships in the dataset were visualised . The pairplot function was used to creates a grid of Axes such that each variable in data will by shared in the y-axis across a single row and in the x-axis across a single column.
And pairwise correlation of all columns in the dataframe found using df.corr()
Feature Importance
Feature importance refers to a class of techniques for assigning scores to input features to a predictive model that indicates the relative importance of each feature when making a prediction.
Feature importance scores can be calculated for problems that involve predicting a numerical value, called regression, and those problems that involve predicting a class label, called classification.
The higher the score more important or relevant is the feature towards your output variable.
Feature importance is an inbuilt class that comes with Tree Based Regressor I used Extra Tree Regressor for extracting required features for the dataset.
Linear Regression
Since the dataset contains more than one independent feature thus it uses multiple linear regression.
Build the Linear regression model
Predict Dependent variables(2.5 AQI)
Evaluate model by finding R-Squared value is also known as the Coefficient of Determination. The value of R-Squared ranges from 0 to 1.The higher the R-Squared value of a model, the better is the model fitting on the data.
However, if the R-Squared value is very close to 1, then there is a possibility of model overfitting, which should be avoided. A good model should have an R-Squared above 0.8.
We get R-Squared value close to 7
Ridge Regression
Steps
Build the Linear regression model
We generally use a scorer object with the scoring parameter in the cross_val_score function. All scorer objects follow the convention that higher return values are better than lower return values.
Thus metrics which measure the distance between the model and the data, like metrics.mean_squared_error, are available as neg_mean_squared_error which return the negated value of the metric.
here we have mse=-2760.392573715005
Try if we could make our model better by hyper tuning it using Ridge Regression.
in Ridege Regression we have taken the alpha(λ-parameter) value as a bunch of possible random values. We are doing GridSearch to find the best parameters which will work for the Ridge Regressor.
Here also the value for the scoring parameter will be neg_mean_squared_error which measures the distance between
the model and the data.
The formula for Ridge Regression is:
