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<p>The aim of this work was evaluate the impact of surface ozone (O<sub>3</sub>) and its precursor gases oxides of Nitrogen (NO<sub>X</sub> = NO + NO<sub>2</sub>) and Carbon monoxide (CO) at ground level corresponding seasonal variations and predict their concentration through the development of mathematical models on Multiple Linear Regression (MLR) and Artificial Neural Network (ANN). This study was carried out at an urban site in Hyderabad (17.47 <sup>0</sup>N, 78.58 <sup>0</sup>E). To evaluate the impact of rising concentration of O<sub>3</sub> and its precursor gases on human health in the premises of the study area, a survey was carried out to collect the data related to patients admitted in hospitals due to various illnesses. The O<sub>3</sub> concentration was observed during pre-monsoon followed by winter and monsoon 54.6 ± 12.8&#xa0;ppb, 48.2 ± 10.1&#xa0;ppb and 40.5 ± 16.9&#xa0;ppb respectively, solar radiation was high in pre-monsoon with 785.3 ± 176.4 W/m<sup>−2</sup>. Observations of NO<sub>X</sub> and CO gases showed the highest mean value during winter i.e., 29.4 ± 10.4&#xa0;ppb, pre-monsoon and monsoon i.e. 1406.5 ± 259.4&#xa0;ppb respectively. The MLR and ANN models revealed that the mean concentrations of O<sub>3</sub> in different seasons followed decreasing trend from pre-monsoon to winter i.e., pre-monsoon &gt; monsoon &gt; winter. It is observed that temperature is negatively correlated with NO<sub>X</sub> and positively correlated with O<sub>3</sub> and the relative humidity showed positive correlation with NO<sub>X</sub> and negative correlation with O<sub>3</sub>. The ANN model was more accurate in predicting O<sub>3</sub> concentrations across seasons, with R<sup>2</sup> values ranging from 0.801 to 0.991, while the MLR model had R<sup>2</sup> values between 0.770 and 0.841. Both models performed well, but the ANN model was the most accurate.</p> Graphical Abstract <p></p>

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Impact of ozone concentration and its precursor gases on environment and human health with seasonal variations at ground level: development of mathematical models based on MLR and ANN

  • 期刊论文

  • 发表日期：

    2025年6月19日

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  • Sarat Kumar Allu,
  • Aparna Reddy,
  • Sameena Begum,
  • Gangagni Rao Anupoju

摘要

The aim of this work was evaluate the impact of surface ozone (O3) and its precursor gases oxides of Nitrogen (NOX = NO + NO2) and Carbon monoxide (CO) at ground level corresponding seasonal variations and predict their concentration through the development of mathematical models on Multiple Linear Regression (MLR) and Artificial Neural Network (ANN). This study was carried out at an urban site in Hyderabad (17.47 0N, 78.58 0E). To evaluate the impact of rising concentration of O3 and its precursor gases on human health in the premises of the study area, a survey was carried out to collect the data related to patients admitted in hospitals due to various illnesses. The O3 concentration was observed during pre-monsoon followed by winter and monsoon 54.6 ± 12.8 ppb, 48.2 ± 10.1 ppb and 40.5 ± 16.9 ppb respectively, solar radiation was high in pre-monsoon with 785.3 ± 176.4 W/m−2. Observations of NOX and CO gases showed the highest mean value during winter i.e., 29.4 ± 10.4 ppb, pre-monsoon and monsoon i.e. 1406.5 ± 259.4 ppb respectively. The MLR and ANN models revealed that the mean concentrations of O3 in different seasons followed decreasing trend from pre-monsoon to winter i.e., pre-monsoon > monsoon > winter. It is observed that temperature is negatively correlated with NOX and positively correlated with O3 and the relative humidity showed positive correlation with NOX and negative correlation with O3. The ANN model was more accurate in predicting O3 concentrations across seasons, with R2 values ranging from 0.801 to 0.991, while the MLR model had R2 values between 0.770 and 0.841. Both models performed well, but the ANN model was the most accurate.

Graphical Abstract