Outdoor and Indoor Ozone Concentration Estimation Based on Artificial Neural Network and Single Zone Mass Balance Model

A comparative analysis of linear regression, neural networks and random forest regression for predicting air ozone employing soft sensor models

The proposed methodology presents a comprehensive analysis of soft sensor modeling techniques for air ozone prediction. We compare the performance of three different modeling techniques: LR (linear regression), NN (neural networks), and RFR (random forest regression). Additionally, we evaluate the impact of different variable sets on prediction performance. Our findings indicate that neural network models, particularly the RNN (recurrent neural networks), outperform the other modeling techniques in terms of prediction accuracy. The proposed methodology evaluates the impact of different variable sets on prediction performance, finding that variable set E demonstrates exceptional performance and achieves the highest average prediction accuracy among various software sensor models. In comparing variable set E and A, B, C, D, it is observed that the inclusion of an additional input feature, PM10, in the latter sets does not improve overall performance, potentially due to multicollinearity between PM10 and PM2.5 variables. The proposed methodology provides valuable insights into soft sensor modeling for air ozone prediction.Among the 72 sensors, sensor NNR[Y]C outperforms all other evaluated sensors, demonstrating exceptional predictive performance with an impressive R2 of 0.8902, low RMSE of 24.91, and remarkable MAE of 19.16. With a prediction accuracy of 81.44%, sensor NNR[Y]C is reliable and suitable for various technological applications.

Temporal variation analysis, impact of COVID-19 on air pollutant concentrations, and forecasting of air pollutants over the cities of Bangalore and Delhi in India

Indian cities are highly vulnerable to atmospheric pollution in recent years, due to exponential growth in urbanisation and industrialisation, and the increased pollution has been made to focus on the temporal variation analysis and forecasting of air pollutants over major Indian cities like Delhi and Bangalore. PM_2.5 concentrations are nearly 60.5% less than the annual average value during monsoon season while 76.3% more during the winter months. Ozone concentrations increase during the summer months (~ 46.3% more than the annual average) in Delhi, whereas in Bangalore, ozone concentrations are more (~ 75% more than the annual average) during the winter months. Variations of carbon monoxide and nitrogen oxides are significantly less comparatively. COVID-19 lockdown has a substantial positive impact on air pollution. Air pollutant concentrations are reduced during phase I and phase II of the lockdown. Pollutants, especially NOx and PM_2.5 concentrations, are drastically reduced compared to the previous years. NOx concentrations are reduced by ~ 20% in Bangalore, whereas ~ 50% in Delhi. PM_2.5 concentrations are reduced by ~ 41% in Delhi and ~ 55% in Bangalore. Forecasting of pollutants will be helpful in providing the valuable information for the optimal air pollution control strategies. It has been observed that linear model gives better results compared to ARIMA and Exponential Smoothening models. By forecasting, the concentration of NO₂ is 115.288 µg/m³, the ozone is 30.636 µg/m³, SO₂ is 11.798 µg/m³, and CO is 2.758 mg/m³ over Delhi in 2021. All the pollutants during forecasting showed a rising trend except sulphur dioxide.

Personal exposure to ambient PM2.5, PM10, O3, NO2, and SO2 for different populations in 31 Chinese provinces

Most epidemiological studies usually employ ambient air pollutant concentrations as a proxy of personal exposure to air pollutants originating outdoors, which could lead to a biased estimation of health effects. Herein, we modeled infiltration and exposure factors as the modifications of personal exposure to ambient PM_2.5, PM₁₀, O₃, NO₂, and SO₂ for all seasons, genders, and ages in 31 Chinese provinces. The annual average exposure factors of PM₁₀, PM_2.5, O₃, NO₂, and SO₂ were 0.42 ± 0.13 (arithmetic mean ± standard deviation), 0.68 ± 0.14, 0.34 ± 0.12, 0.50 ± 0.14, and 0.40 ± 0.13, respectively. We observed significant age, gender, seasonal, and geographical differences in infiltration and exposure factors for all studied ambient air pollutants. These factors were higher in southern China than in the north, and they were the highest in summer and the lowest in winter. The exposure factor of minors (age < 18 years) was significantly lower than that of adults (age ≥ 18 years, P < 0.01). Adult males had higher exposure factors than females (P < 0.01). Epidemiological studies utilizing outdoor concentrations of air pollutants could overestimate personal exposure to these pollutants. The present study could help in reducing the bias in the estimation of the health effects of air pollutants.

Effect of exogenous catechin on alleviating O3 stress: The role of catechin-quinone in lipid peroxidation, salicylic acid, chlorophyll content, and antioxidant enzymes of Zamioculcas zamiifolia

Ozone (O₃) can cause oxidative stress in plants and humans. Catechin is an antioxidant that enriches tea and can probably increase O₃ tolerance in plants. To investigate the mechanism of catechin to alleviate O₃ stress in plants, Zamiocalcus zamiifolia (an efficient plant for O₃ phytoremediation) was sprayed with 5 mM catechin and was used to expose O₃ (150-250) under long-term operation (10 cycles). We investigated whether exogenous catechin could enhance O₃ removal and alleviate O₃ stress through a balanced redox state in plants. Z. zamiifolia sprayed with catechin exhibited higher O₃ removal (80.27±3.12%), than Z. zamiifolia without catechin (50.03±2.68%). O₃ in the range of 150-250 ppb led to stress in plants, as shown by an increased malondialdehyde content (MDA) and salicylic acid (SA). Whereas under the presence of O₃, exogenous catechin could maintain the MDA content and inhibit SA accumulation. Under Z. zamiifolia+catechin+O₃ conditions, catechin reacted with O₃, which led to the formation of catechin-quinone. The formation of catechin-quinone was confirmed by the depletion of reduced glutathione content (GSH). This catechin-quinone could induce GST and APX genes that are up-regulated approximately 35- and 5-fold, respectively. Hence, Z. zamiifolia+catechin+O₃ conditions had higher performance for coping with oxidative stress than did Z. zamiifolia+O₃ conditions. This evidence demonstrates that catechin could enhance O₃ removal through a balanced redox state in plant cells. Finally, the application of tea extract for enhanced O₃ removal is also shown in this study.