Characterization of Surface Ozone Behavior at Different Regimes

Exploring ozone formation rules and concentration response to the change of precursors based on artificial neural network simulation in a typical industrial park

Industrial parks have more complex O3 formation mechanisms due to a higher concentration and more dense emission of precursors. This study establishes an artificial neural network (ANN) model with good performance by expanding the moment and concentration changes of pollutants into general variables of meteorological factors and concentrations of pollutants. Finally, the O3 formation rules and concentration response to the changes of volatile organic compounds (VOCs) and nitrogen oxides (NOx) was explored. The results showed that the studied area belonged to the NOx-sensitive regime and the sensitivity was strongly affected by relative humidity (RH) and pressure (P). The concentration of O3 tends to decrease with a higher P, lower temperature (Temp), and medium to low RH when nitric oxide (NO) is added. Conversely, at medium P, high Temp, and high RH, the addition of nitrogen dioxide (NO2) leads to a larger decrease capacity in O3 concentration. More importantly, there is a local reachable maximum incremental reactivity (MIRL) at each certain VOCs concentration level which linearly increased with VOCs. The general maximum incremental reactivity (MIR) may lead to a significant overestimation of the attainable O3 concentration in NOx-sensitive regimes. The results can significantly support the local management strategies for O3 and the precursors control.

Microalgal cultures for the remediation of wastewaters with different nitrogen to phosphorus ratios: Process modelling using artificial neural networks

Microalgae have remarkable potential for wastewater bioremediation since they can efficiently uptake nitrogen and phosphorus in a sustainable and environmentally friendly treatment system. However, wastewater composition greatly depends on its source and has a significant seasonal variability. This study aimed to evaluate the impact of different N:P molar ratios on the growth of Chlorella vulgaris and nutrient removal from synthetic wastewater. Furthermore, artificial neural network (ANN) threshold models, optimised by genetic algorithms (GAs), were used to model biomass productivity (BP) and nitrogen/phosphorus removal rates (RR_N/RR_P). The impact of various inputs culture variables on these parameters was evaluated. Microalgal growth was not nutrient limited since the average biomass productivities and specific growth rates were similar between the experiments. Nutrient removal efficiencies/rates reached 92.0 ± 0.6%/6.15 ± 0.01 mg_N L^-1 d^-1 for nitrogen and 98.2 ± 0.2%/0.92 ± 0.03 mg_P L^-1 d^-1 for phosphorus. Low nitrogen concentration limited phosphorus uptake for low N:P ratios (e.g., 2 and 3, yielding 36 ± 2 mg_DW mg_P^-1 and 39 ± 3 mg_DW mg_P^-1, respectively), while low phosphorus concentration limited nitrogen uptake with high ratios (e.g., 66 and 67, yielding 9.0 ± 0.4 mg_DW mg_N^-1 and 8.8 ± 0.3 mg_DW mg_N^-1, respectively). ANN models showed a high fitting performance, with coefficients of determination of 0.951, 0.800, and 0.793 for BP, RR_N, and RR_P, respectively. In summary, this study demonstrated that microalgae could successfully grow and adapt to N:P molar ratios between 2 and 67, but the nutrient uptake was impacted by these variations, especially for the lowest and highest N:P molar ratios. Furthermore, GA-ANN models demonstrated to be relevant tools for microalgal growth modelling and control. Their high fitting performance in characterising this biological system can contribute to reducing the experimental effort for culture monitoring (human resources and consumables), thus decreasing the costs of microalgae production.

Current Status and Future Forecast of Short-lived Climate-Forced Ozone in Tehran, Iran, derived from Ground-Based and Satellite Observations

In this study, the distribution and alterations of ozone concentrations in Tehran, Iran, in 2021 were investigated. The impacts of precursors (i.e., CO, NO₂, and NO) on ozone were examined using the data collected over 12 months (i.e., January 2021 to December 2021) from 21 stations of the Air Quality Control Company (AQCC). The results of monthly heat mapping of tropospheric ozone concentrations indicated the lowest value in December and the highest value in July. The lowest and highest seasonal concentrations were in winter and summer, respectively. Moreover, there was a negative correlation between ozone and its precursors. The Inverse Distance Weighting (IDW) method was then implemented to obtain air pollution zoning maps. Then, ozone concentration modeled by the IDW method was compared with the average monthly change of total column density of ozone derived from Sentinel-5 satellite data in the Google Earth Engine (GEE) cloud platform. A good agreement was discovered despite the harsh circumstances that both ground-based and satellite measurements were subjected to. The results obtained from both datasets showed that the west of the city of Tehran had the highest averaged O₃ concentration. In this study, the status of the concentration of ozone precursors and tropospheric ozone in 2022 was also predicted. For this purpose, the Box-Jenkins Seasonal Autoregressive Integrated Moving Average (SARIMA) approach was implemented to predict the monthly air quality parameters. Overall, it was observed that the SARIMA approach was an efficient tool for forecasting air quality. Finally, the results showed that the trends of ozone obtained from terrestrial and satellite observations throughout 2021 were slightly different due to the contribution of the tropospheric ozone precursor concentration and meteorology conditions.

Surface Ozone Pollution: Trends, Meteorological Influences, and Chemical Precursors in Portugal

Surface ozone (O3) is a secondary air pollutant, harmful to human health and vegetation. To provide a long-term study of O3 concentrations in Portugal (study period: 2009–2019), a statistical analysis of ozone trends in rural stations (where the highest concentrations can be found) was first performed. Additionally, the effect of nitrogen oxides (NOx) and meteorological variables on O3 concentrations were evaluated in different environments in northern Portugal. A decreasing trend of O3 concentrations was observed in almost all monitoring stations. However, several exceedances to the standard values legislated for human health and vegetation protection were recorded. Daily and seasonal O3 profiles showed high concentrations in the afternoon and summer (for all inland rural stations) or spring (for Portuguese islands). The high number of groups obtained from the cluster analysis showed the difference of ozone behaviour amongst the existent rural stations, highlighting the effectiveness of the current geographical distribution of monitoring stations. Stronger correlations between O3, NO, and NO2 were detected at the urban site, indicating that the O3 concentration was more NOx-sensitive in urban environments. Solar radiation showed a higher correlation with O3 concentration regarding the meteorological influence. The wind and pollutants transport must also be considered in air quality studies. The presented results enable the definition of air quality policies to prevent and/or mitigate unfavourable outcomes from O3 pollution.

Empirical ozone isopleths at urban and suburban sites through evolutionary procedure-based models

Ozone (O₃) is a reactive oxidant that causes chronic effects on human health, vegetation, ecosystems and materials. This study aims to create O₃ isopleths in urban and suburban environments, based on machine learning with air quality data collected from 2001 to 2017 at urban (EA) and suburban (CC) monitoring stations from Madrid (Spain). Artificial neural network (ANN) models have powerful fitting performance, describing correctly several complex and nonlinear relationships such as O₃ and his precursors (VOC and NO_x). Also, ANN learns from the experience provided by data, contrary to mechanistic models based on the fundamental laws of natural sciences. The determined isopleths showed a different behaviour of the VOC-NO_x-O₃ system compared to the one achieved with a mechanistic model (EKMA curve): e.g. for constant NO_x concentrations, O₃ concentrations decreased with VOC concentrations in the ANN model. Considering the difficulty to model all the phenomena (and acquired all the required data) that influences O₃ concentrations, the statistical models may be a solution to describe this system correctly. The applied methodology is a valuable tool for defining mitigation strategies (control of precursors' emissions) to reduce O₃ concentrations. However, as these models are obtained by air quality data, they are not geographical transferable.

Influence of the meteorological conditions and some pollutants on PM10 concentrations in Lamphun, Thailand

Particulate matter (PM) has been occurring regularly during the dry season in the upper north of Thailand including Lamphun Province that might be influenced by various factors including climatologic and other pollutants. This paper aims to investigate the climatologic and gaseous factors influencing the occurrence of PM₁₀ concentration using Pollution Control Department (PCD) data. The secondary data of 2009 to 2017 obtained from the PCD was used for analysis. We used descriptive statistics, Pearson's correlation coefficient, multiple regression and graphic presentation using R program (R packages of 'open air' and 'ncdf4') and Microsoft Excel Spreadsheet®. In addition, the periodic measurement of PM_2.5 and PM₁₀ were investigated to determine the ratio of PM_2.5/PM₁₀. The results indicated that haze episodes (daily PM₁₀ concentration always over the PCD standard) normally occur during the dry season from February to April. The maximum concentration was always found in March. The PM₁₀ concentration was negatively associated with relative humidity and temperature while the PM₁₀ concentration showed a strongly positive association with CO and NO₂ concentration with correlation values of 0.70 and 0.57, respectively. Furthermore, we found CO and PM₁₀ concentration was associated with ozone concentration. This finding will benefit local communities and the public health sector to provide a warning system for preparation and response plans to react to PM₁₀ episodes in their responsible areas.

A Machine Learning Approach to Investigate the Surface Ozone Behavior

The concentration of surface ozone (O3) strongly depends on environmental and meteorological variables through a series of complex and non-linear functions. This study aims to explore the performances of an advanced machine learning (ML) method, the boosted regression trees (BRT) technique, in exploring the relationships between surface O3 and its driving factors, and in predicting the levels of O3 concentrations. To this end, a BRT model was trained on hourly data of air pollutants and meteorological parameters, acquired, over the 2016–2018 period, in a rural area affected by an anthropic source of air pollutants. The abilities of the BRT model in ranking, visualizing, and predicting the relationship between ground-level O3 concentrations and its driving factors were analyzed and illustrated. A comparison with a multiple linear regression (MLR) model was performed based on several statistical indicators. The results obtained indicated that the BRT model was able to account for 81% of changes in O3 concentrations; it slightly outperforms the MLR model in terms of the predictions accuracy and allows a better identification of the main factors influencing O3 variability on a local scale. This knowledge is expected to be useful in defining effective measures to prevent and/or mitigate the health damages associated with O3 exposure.

Analysis and Modelling of PM2.5 Temporal and Spatial Behaviors in European Cities

Particulate matter with an aerodynamic diameter of less than 2.5 µm (PM2.5) is associated with adverse effects on human health (e.g., fatal cardiovascular and respiratory diseases), and environmental concerns (e.g., visibility impairment and damage in ecosystems). This study aimed to evaluate temporal and spatial trends and behaviors of PM2.5 concentrations in different European locations. Statistical threshold models using Artificial Neural Networks (ANN) defined by Genetic Algorithms (GA) were also applied for an urban centre site in Istanbul, to evaluate the influence of meteorological variables and PM10 concentrations on PM2.5 concentrations. Lower PM2.5 concentrations were observed in northern Europe. The highest values were found at traffic-related sites. PM2.5 concentrations were usually higher during the winter and tended to present strong increases during rush hours. PM2.5/PM10 ratios were slightly higher at background sites and the lower values were found in northern Europe (Helsinki and Stockholm). Ratios were usually higher during cold months and during the night. The statistical model (ANN + GA) allowed evaluating the combined effect of different explanatory variables (temperature, wind speed, relative humidity, air pressure and PM10 concentrations) on PM2.5 concentrations, under different regimes defined by relative humidity (threshold value of 79.1%). Important information about the temporal and spatial trends and behaviors related to PM2.5 concentrations in different European locations was developed.