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Related Concept Videos

Linear Approximations01:23

Linear Approximations

For a differentiable function of two variables, linear approximation estimates values near a known point by replacing the curved surface with its tangent plane. Consider the function\begin{equation*}f(x,y)=x^2+3y^2\end{equation*}near the point (2, 1). The exact value at this point is f(2, 1) = 22 + 3(1)2 = 4 + 3 = 7.The linear approximation of f(x, y)) near (a, b) is\begin{equation*}L(x,y)=f(a,b)+f_x(a,b)(x-a)+f_y(a,b)(y-b)\end{equation*}First, compute the partial derivatives: fx(x, y) = 2x and...

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Updated: Jul 9, 2026

Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions
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Published on: January 7, 2019

Forecasting ambient PM2.5 and PM10 concentrations in Hisar City through machine learning.

Munish Kumar1, Parveen Sihag2, Sergij Vambol3

  • 1Department of Civil Engineering, Guru Kashi University Talwandi Sabo, Bathinda, 151302, India.

Scientific Reports
|July 7, 2026
PubMed
Summary

Machine learning models accurately predict particulate matter (PM2.5 and PM10) in Hisar City. Ensemble methods like Gradient Boosting and Random Forest show superior performance for reliable air quality forecasting and management.

Keywords:
Air Quality PredictionHisar CityMachine LearningPM10PM2.5

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Last Updated: Jul 9, 2026

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Area of Science:

  • Environmental Science
  • Data Science
  • Atmospheric Chemistry

Background:

  • Hisar City faces significant PM2.5 and PM10 pollution from industrial, vehicular, and agricultural sources.
  • Accurate particulate matter forecasting is crucial for effective air quality management strategies.

Purpose of the Study:

  • To evaluate machine learning models for predicting PM2.5 and PM10 concentrations in Hisar City.
  • To identify the most effective models for reliable air quality forecasting.

Main Methods:

  • Collected historical air quality and meteorological data (2020-2024).
  • Trained and tested models including Support Vector Regression, Random Forest, Gradient Boosting, AdaBoost, and M5.
  • Utilized SHAP analysis for model explainability and sensitivity analysis of weather variables.
  • Assessed performance using Correlation Coefficient (CC), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE).

Main Results:

  • Ensemble models (Gradient Boosting and Random Forest) outperformed other regression models.
  • Gradient Boosting achieved CC of 0.73 for PM2.5 and 0.668 for PM10, with minimal RMSE.
  • Random Forest achieved CC of 0.724 for PM2.5 and 0.674 for PM10 during testing.

Conclusions:

  • Machine learning techniques provide a reliable method for predicting air quality.
  • Accurate PM forecasting supports policymakers in developing timely emergency response plans.