Short-term PM2.5 forecasting using a unique ensemble technique for proactive environmental management initiatives
Hasnain Iftikhar, Moiz Qureshi, Justyna Żywiołek, Javier Linkolk López‐Gonzales, Olayan Albalawi
Abstract
Particulate matter with a diameter of 2.5 microns or less ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m2"><mml:msub><mml:mrow><mml:mtext>PM</mml:mtext></mml:mrow><mml:mrow><mml:mn>2.5</mml:mn></mml:mrow></mml:msub></mml:math> ) is a significant type of air pollution that affects human health due to its ability to persist in the atmosphere and penetrate the respiratory system. Accurate forecasting of particulate matter is crucial for the healthcare sector of any country. To achieve this, in the current work, a new time series ensemble approach is proposed based on various linear (autoregressive, simple exponential smoothing, autoregressive moving average, and theta) and nonlinear (nonparametric autoregressive and neural network autoregressive) models. Three ensemble models are also developed, each employing distinct weighting strategies: equal distribution of weight among all single models (ESME), weight assignment based on training average accuracy errors (ESMT), and weight assignment based on validation mean accuracy measures (ESMV). This technique was applied to daily <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m3"><mml:msub><mml:mrow><mml:mtext>PM</mml:mtext></mml:mrow><mml:mrow><mml:mn>2.5</mml:mn></mml:mrow></mml:msub></mml:math> concentration data from 1 January 2019, to 31 May 2023, in Pakistan’s main cities, including Lahore, Karachi, Peshawar, and Islamabad, to forecast short-term <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m4"><mml:msub><mml:mrow><mml:mtext>PM</mml:mtext></mml:mrow><mml:mrow><mml:mn>2.5</mml:mn></mml:mrow></mml:msub></mml:math> concentrations. When compared to other models, the best ensemble model (ESMV) demonstrated mean errors ranging from 3.60% to 25.79% in Islamabad, 0.81%–13.52% in Lahore, 1.08%–7.06% in Karachi, and 1.09%–12.11% in Peshawar. These results indicate that the proposed ensemble approach is more efficient and accurate for short-term <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m5"><mml:msub><mml:mrow><mml:mtext>PM</mml:mtext></mml:mrow><mml:mrow><mml:mn>2.5</mml:mn></mml:mrow></mml:msub></mml:math> forecasting than existing models. Furthermore, using the best ensemble model, a forecast was made for the next 15 days (June 1 to 15 June 2023). The forecast showed that in Lahore, the highest <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m6"><mml:msub><mml:mrow><mml:mtext>PM</mml:mtext></mml:mrow><mml:mrow><mml:mn>2.5</mml:mn></mml:mrow></mml:msub></mml:math> value (236.00 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m7"><mml:mi>μ</mml:mi><mml:mi>g</mml:mi><mml:mo>/</mml:mo><mml:msup><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:math> ) was observed on 8 June 2023. Other days also displayed higher and poor air quality throughout the 15 days. Conversely, Karachi experienced moderate <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m8"><mml:msub><mml:mrow><mml:mtext>PM</mml:mtext></mml:mrow><mml:mrow><mml:mn>2.5</mml:mn></mml:mrow></mml:msub></mml:math> concentration levels between 50 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m9"><mml:mi>μ</mml:mi><mml:mi>g</mml:mi><mml:mo>/</mml:mo><mml:msup><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:math> and 80 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m10"><mml:mi>μ</mml:mi><mml:mi>g</mml:mi><mml:mo>/</mml:mo><mml:msup><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:math> . In Peshawar, the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m11"><mml:msub><mml:mrow><mml:mtext>PM</mml:mtext></mml:mrow><mml:mrow><mml:mn>2.5</mml:mn></mml:mrow></mml:msub></mml:math> concentration levels were consistently unhealthy, with the highest peak (153.00 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m12"><mml:mi>μ</mml:mi><mml:mi>g</mml:mi><mml:mo>/</mml:mo><mml:msup><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:math> ) observed on 9 June 2023. This forecasting experience can assist environmental monitoring organizations in implementing cost-effective planning to minimize air pollution.