Ozone Time Research Articles

Assessment of surface ozone products from downscaled CAMS reanalysis and CAMS daily forecast using urban air quality monitoring stations in Iran

Abstract. Tropospheric ozone time series consist of the effects of various scales of motion, from meso-scales to large timescales, which are often challenging for global models to capture. This study uses two global datasets, namely the reanalysis and the daily forecast of the Copernicus Atmosphere Monitoring Service (CAMS), to assess the capability of these products in presenting ozone's features on regional scales. We obtained 16 relevant meteorological and several pollutant species, such as O3, CO, NOx, etc., from CAMS. Furthermore, we employed a comprehensive set of in situ measurements of ozone at 27 urban stations in Iran for the year 2020. We decomposed the time series into three spectral components, i.e., short (S), medium (M), and long (L) terms. To cope with the scaling issue between the measured data and the CAMS' products, we developed a downscaling approach based on a long short-term memory (LSTM) neural network method which, apart from modeled ozone, also assimilated meteorological quantities as well as lagged O3 observations. Results show the benefit of applying the LSTM method instead of using the original CAMS products for providing O3 over Iran. It is found that lagged O3 observation has a larger contribution than other predictors in improving the LSTM. Compared with the S, the M component shows more associations with observations, e.g., correlation coefficients larger than 0.7 for the S and about 0.95 for the M in both models. The performance of the models varies across cities; for example, the highest error is for areas with high emissions of O3 precursors. The robustness of the results is confirmed by performing an additional downscaling method. This study demonstrates that coarse-scale global model data, such as CAMS, need to be downscaled for regulatory purposes or policy applications at local scales. Our method can be useful not only for the evaluation but also for the prediction of other chemical species, such as aerosols.

Investigating Zonal Asymmetries in Stratospheric Ozone Trends From Satellite Limb Observations and a Chemical Transport Model

AbstractThis study investigates the origin of a zonal asymmetry in stratospheric ozone trends at northern high latitudes, identified in satellite limb observations over the past two decades. We use a merged data set consisting of ozone profiles retrieved at the University of Bremen from SCIAMACHY and OMPS‐LP measurements to derive ozone trends. We also use TOMCAT chemical transport model (CTM) simulations, forced by ERA5 reanalyses, to investigate the factors that drive the asymmetry observed in the long‐term changes. By studying seasonally and longitudinally resolved observation‐based ozone trends, we find, especially during spring, a well‐pronounced asymmetry at polar latitudes with values up to +6 % per decade over Greenland and −5 % per decade over western Russia. The control CTM simulation agrees well with these observed trends, whereas sensitivity simulations indicate that chemical mechanisms involved in the production and removal of ozone, or their changes, are unlikely to explain the observed behavior. The decomposition of TOMCAT ozone time series and ERA5 geopotential height into the first two wavenumber components shows a clear correlation between the two variables in the middle stratosphere and demonstrates a weakening and a shift in the wavenumber‐1 planetary wave activity over the past two decades. Finally, the analysis of the polar vortex position and strength points to a decadal oscillation with a reversal pattern at the beginning of the century. The same is found in the ozone trend asymmetry. This further stresses the link between changes in the polar vortex position and the identified ozone trend pattern.

Effect of aqueous ozone treatment on the reduction of chlorpyrifos and physicochemical and microbial qualities of cucumber (Cucumis sativusL.): Process modeling and optimization

AbstractA study was conducted at different combinations of ozone concentration (50%–100%) and time (10–30 min) to study the effect of aqueous ozone treatment on chlorpyrifos degradation in cucumber. A process was developed and compared using ANN‐GA and RSM techniques. The optimum conditions obtained using RSM were ozone concentration of 95% and treatment time of 10 min. While the optimum conditions achieved by ANN‐GA were ozone concentration of 81% and treatment time of 14 min. However, chlorpyrifos degradation is more at the condition optimized by ANN compared to the condition optimized by RSM. At the optimum condition, the cucumber showed superior physicochemical and microbial qualities, including pesticide degradation (91.08%), color change (3.58), firmness (11.75 N), moisture content (97.49%), water activity (0.93), pH (6.18), titratable acidity (0.06%), total soluble solids (2.1 °Brix), total plate count (1.5 × 105 CFU/g), and yeast/mold count (3 × 105 CFU/g), respectively.Practical applicationsIncreased awareness of the health benefits associated with the consumption of fruits and vegetables has led to a rise in their consumption. These items are primarily consumed in their raw or fresh state and often feature in salads. However, the presence of pesticide residues on the surface of raw fruits and vegetables can pose significant health risks to consumers. Ozone treatment can be used for effectively removing pesticide residue in the fresh‐cut fruits and vegetables industry. Also, ozone treatment can reduce the microbial count on the surface of fruits and vegetables which enhances the shelf life of fruits and vegetables. Furthermore, it can prevent the natural ripening process of commodities, thereby increasing the consumer's acceptance. Ozone is considered a safe and eco‐friendly alternative to chemical disinfectants and preservatives. It leaves no chemical residue on the produce and breaks down into oxygen, leaving no harmful byproducts, thereby ensuring safe consumption of the commodity.

Ozone Assisted Photocatalytic Degradation of Textile Wastewater

In present research, photocatalytic degradation of textile wastewater was investigated through the combined effect of ozonation and photocatalysis processes. Catalytic acids in the nano-particle form under moderate reaction conditions were used for the successful disintegration of contaminants in textile wastewater which in turn increases the process efficiency. Moreover, the combined effect of the photocatalytic-ozonation process after secondary treatment of textile wastewater on discharge requirements of zero discharge hazardous chemicals (ZDHC) by Road to Zero was also studied. Different parameters were optimized like ozone flowrate, catalytic concentration, UV light intensity, and reaction time. Results revealed that overall process efficiency is influenced by ozone and reaction time. In contrast, an increased rate of degradation is attributed to the synthesized TiO2 (anatase) in nanoparticle form. The combined ozone-assisted photo-catalytic process showed the COD, BOD, and TSS removal efficiencies of 91.5%, 94%, and 86% at 25 and#176;C temperature and pH 7-9. The results were attained at optimum conditions like reaction time (140 min), Ozone concentration (2 g/hr), TiO2 concentration (2g/L), and UV energy (88 W), respectively. The study demonstrated the efficiency of an integrated process for the degradation of real textile wastewater. Thus, combined photocatalytic and ozonation processes are known to exhibit better responses than individual processes for pollutants removal from textile wastewater.

The potential use of ozone as antifungal and antiaflatoxigenic agent in nuts and its effect on nutritional quality.

Ozone gas is considered as a safe antimicrobial agent in food industries. Here, we evaluated the antifungal and antiaflatoxigenic activities of ozone against fungal contamination in nuts. The most predominant fungal genera in nuts were Aspergillus, Penicillium, Fusarium, and Rhizopus. Ozone (4 ppm) significantly reduced the fungal sporulation of A. flavus and their aflatoxin production. Interestingly, ozone treatment of nuts reduced the total fungal count and increased aflatoxins degradation by approximately 95% and 85%, respectively. Ozone displayed high efficiency to increase the permeability of cell membrane and injury of cell wall of fungi. Increasing the exposure time of ozone in nuts up to 180 minutes showed to reduce the total lipid, carbohydrates, and protein by around 41.2%, 42.7% and 38.4% respectively, in pistachio, almond and peanuts. In conclusion, ozonation is a suitable decontaminating approach for reducing the microbial load in nuts, when used with suitable exposure time.

Ozone-based in-situ reactive extrusion for rapid functionalisation and degradation of low density polyethylene

Improving the hydrophilicity of low density polyethylene (LDPE) expends its use in blends. LDPE melt was rapidly functionalised and degraded by ozone in twin-screw extruder by varying extrusion times from one to seven and temperatures from 160 °C to 240 °C. Abundant oxygen-containing groups, such as carbonyl group (CO) at 1718 cm−1, hydroxyl group (-OH) at 3400 cm−1 and aldehyde group (-CHO) at 1740 cm−1, were generated into ozone treated PE, resulting in improved hydrophilicity. At 200 °C, ozone treated PE exhibited a high melt flow index (21.5 g/10 min) and low complex viscosity modulus due to molecular degradation. Nevertheless, cross-linking occurred at a higher temperature of 240 °C. A higher crystal peak temperature of ozone treated PE at 200 °C was attributed to the introduction of oxygen atoms. The poor thermal stability of ozone treated PE was evidenced by a lower initial decomposition temperature. The oxidation induction time of ozone treated PE at 200 °C declined from 32 min for one extrusion to 12 min for seven extrusions demonstrating a vulnerability of ageing. Increasing the extrusion times gradually enhanced the tensile modulus of the ozone treated PE at 200 °C, while raising the treatment temperature had no effect on the tensile properties. Rapid functionalisation and degradation of LDPE were successfully achieved using ozone in reactive extrusion, thus broadening the applications of ozone treated PE in composites.

Effect of Gaseous Ozone and Storage Time on Polyphenolic Profile and Sugar Content in Fruits of Selected Vaccinium corymbosum L. Genotypes.

One of the best sources of antioxidant and health-promoting bioactive substances is the fruit of V. corymbosum. A potent oxidizing agent, ozone (O3), can effectively eliminate bacteria. The application of ozone gas to V. corymbosum fruit during storage had a favorable impact on the fruit's phenolic component and sugar content in the current investigation. After 7 days of storage, phenolic content in all highbush blueberry cultivars and clones tested increased on average by 28.60%, including anthocyanins by 34%. After 14 days of storage, an average increase of 16.50% in phenolic compounds was observed, including a 20.53% increase in anthocyanins. Among all the tested varieties, clone BOR-21 treated with a dose of 0.01 mL·L-1 ozone for 30 min after 14 days had the highest TPC-143.73 mg·100 g-1 f.w. The sugar content of berries treated with a dose of 0.01 mL·L-1 ozone for 30 min, on day 7 and day 14 of storage increased by 9.2% and 6.3%, respectively. On day 7, the highest amount of total sugar (22.74 g·100 g-1) was observed in Duke cultivar after being exposed to 0.01 mL·L-1 ozone for 15 min. The ozonation treatments enhanced the fruit's saturation with nutrients, which raises the fruit's value as food.

Evaluation of ozone treatment for bacterial disinfection of ballast water

In 2004, the International Maritime Organization (IMO) adopted the International Convention for the Control and Management of Ballast Water and Sediments (BWMC), aiming to mitigate the spread of organisms through ballast water. In this study, ozone has been employed as the oxidizing agent in ballast water, both on its own and in combination with hydrogen peroxide (H2O2) and peroxymonopersulfate (PMS). Disinfection is carried out using a discontinuous method, targeting Vibrio alginolyticus and Enterococcus faecalis. The application of ozone at a concentration of 0.7 mg/L in seawater achieved 4 log-reductions of E. faecalis in less than 4 min. In the case of V. alginolyticus in seawater, the application of 0.6 mg/L of ozone and a contact time of 3 min was sufficient to achieve a reduction of 4 logarithmic units. This highlights the greater resistance of E. faecalis to the treatment compared to V. alginolyticus. In order to achieve the same level of disinfection for E. faecalis, a higher concentration of ozone and longer exposure time are required. Combining ozone with either H2O2 or PMS did not improve treatment. Based on the treatments presented in this study, it would be feasible to treat ballast water with an E. faecalis concentration of up to 3.7·106 CFU/100 mL and a V. alginolyticus concentration of up to 4.25·104 CFU/100 mL and still meet the levels required by BWMC. These concentrations are much higher than those found commonly in the marine environment, so the treatment would reduce the concentrations to levels below the D-2 standard.

Effect of atmospheric cold plasma treatment modes on the quality of red shrimp (Solenocera crassicornis) during cold chain storage

An increasingly popular non-thermal approach for food preservation is atmospheric cold plasma (ACP), which can efficiently inactivate various microorganisms. This study explored the impact of different ACP treatment modes on the preservation of red shrimp in cold chain storage. A novel treatment mode, ACP cyclical, was introduced as an alternative to ACP one-time treatment. The results indicated that the ACP cyclical treatment extended the ozone and shrimp exposure time to 35 min compared to one-time treatment, resulting in a notable enhancement in preservation effectiveness. While ACP treatment significantly reduced the total viable count (TVC) of red shrimp at day 0, it does not affect total volatile basic-nitrogen (TVB-N), pH value, or color. At day 8, the ACP cyclical group showed a 17.38% reduction in TVB-N, 36.90% decrease in TVC, and 32.39% decline in sulfur-producing bacteria compared to the ACP one-time treatment group. Electronic nose analysis indicated improved odor stability in both ACP treatment modes. Moreover, correlation and partial least squares discriminant analysis revealed that ACP cyclical treatment delayed shrimp’ color deterioration and TVB-N increase during prolonged storage. This study highlighted the potential of ACP cyclical treatment for enhancing red shrimp preservation in cold chain storage.

Can Ozone Dissociate at the Surface of Water (Water Droplet and Ice) without Light?

Ozone is a major source of OH radicals in the troposphere. It is well-known that photodissociation of ozone is key for the conversion of ozone into OH radicals. In the present study, using Born-Oppenheimer molecular dynamics simulation, we have shown that on the surface of the droplet and ice, ozone can dissociate without light. In addition, the dissociation time of ozone is found to be much less on the ice surface than the same time on the water droplet. As the dissociation of ozone on the water surface can happen during the day as well as in the night time, we believe this route of forming OH radicals can be even more important than the photodissociation. The present study suggests that the cloud and ice surface can enhance the oxidizing power of the troposphere.

Total column ozone trends from the NASA Merged Ozone time series 1979 to 2021 showing latitude-dependent ozone recovery dates (1994 to 1998)

Abstract. Monthly averaged total column ozone data (ΩMOD(t,θ)) from the NASA Merged Ozone Data Set (MOD) were examined to show that the latitude-dependent (θ) ozone depletion turnaround dates (TA(θ)) range from 1994 to 1998. TA(θ) is defined as the approximate date when the zonally averaged ozone ceased decreasing. ΩMOD data used in this study were created by combining data from Solar Backscattered Ultraviolet instruments (SBUV/SBUV-2) and the Ozone Mapping and Profiler Suite (OMPS-NP) from 1979 to 2021. The newly calculated systematic latitude-dependent hemispherically asymmetric TA(θ) shape currently does not appear in the suite of chemistry–climate models that are part of the Chemistry–Climate Model Validation Activity (CCMVal), which combines the effects of photochemistry, volcanic eruptions, and dynamics in their estimate of ozone recovery. Trends of zonally averaged total column ozone in percent per decade were computed before and after TA(θ) using two different trend estimate methods that closely agree, Fourier series multivariate linear regression and linear regression on annual averages. During the period 1979 to TA(θ), the most dramatic rates of Southern Hemisphere (SH) ozone loss were PD=-10.9±3 % per decade at 77.5∘ S and -8.0±1.1 % per decade at 65∘ S, which is about double the Northern Hemisphere (NH) rate of loss of PD=-5.6±4 % per decade at 77.5∘ N and 4.4±1 % per decade at 65∘ N for the period 1979 to TA(θ). After TA(θ), there was an increase at 65∘ S of PD=1.6±1.4 % per decade with smaller increases from 55 to 25∘ S and a small decrease at 35∘ N of -0.4±0.3 % per decade. Except for the Antarctic region, there only has been a small recovery in the SH toward 1979 ozone values and almost none in the NH.

Short-Term Forecasting of Ozone Concentration in Metropolitan Lima Using Hybrid Combinations of Time Series Models

In the modern era, air pollution is one of the most harmful environmental issues on the local, regional, and global stages. Its negative impacts go far beyond ecosystems and the economy, harming human health and environmental sustainability. Given these facts, efficient and accurate modeling and forecasting for the concentration of ozone are vital. Thus, this study explores an in-depth analysis of forecasting the concentration of ozone by comparing many hybrid combinations of time series models. To this end, in the first phase, the hourly ozone time series is decomposed into three new sub-series, including the long-term trend, the seasonal trend, and the stochastic series, by applying the seasonal trend decomposition method. In the second phase, we forecast every sub-series with three popular time series models and all their combinations In the final phase, the results of each sub-series forecast are combined to achieve the results of the final forecast. The proposed hybrid time series forecasting models were applied to four Metropolitan Lima monitoring stations—ATE, Campo de Marte, San Borja, and Santa Anita—for the years 2017, 2018, and 2019 in the winter season. Thus, the combinations of the considered time series models generated 27 combinations for each sampling station. They demonstrated significant forecasts of the sample based on highly accurate and efficient descriptive, statistical, and graphic analysis tests, as a lower mean error occurred in the optimized forecast models compared to baseline models. The most effective hybrid models for the ATE, Campo de Marte, San Borja, and Santa Anita stations were identified based on their superior out-of-sample forecast results, as measured by RMSE (4.611, 3.637, 1.495, and 1.969), RMSPE (4.464, 11.846, 1.864, and 15.924), MAE (1.711, 2.356, 1.078, and 1.462), and MAPE (14.862, 20.441, 7.668, and 76.261) errors. These models significantly outperformed other models due to their lower error values. In addition, the best models are statistically significant (p < 0.05) and superior to the rest of the combination models. Furthermore, the final proposed models show significant performance with the least mean error, which is comparatively better than the considered baseline models. Finally, the authors also recommend using the proposed hybrid time series combination forecasting models to predict ozone concentrations in other districts of Lima and other parts of Peru.

Graph Machine Learning for Improved Imputation of Missing Tropospheric Ozone Data.

Gaps in the measurement series of atmospheric pollutants can impede the reliable assessment of their impacts and trends. We propose a new method for missing data imputation of the air pollutant tropospheric ozone by using the graph machine learning algorithm "correct and smooth". This algorithm uses auxiliary data that characterize the measurement location and, in addition, ozone observations at neighboring sites to improve the imputations of simple statistical and machine learning models. We apply our method to data from 278 stations of the year 2011 of the German Environment Agency (Umweltbundesamt - UBA) monitoring network. The preliminary version of these data exhibits three gap patterns: shorter gaps in the range of hours, longer gaps of up to several months in length, and gaps occurring at multiple stations at once. For short gaps of up to 5 h, linear interpolation is most accurate. Longer gaps at single stations are most effectively imputed by a random forest in connection with the correct and smooth. For longer gaps at multiple stations, the correct and smooth algorithm improved the random forest despite a lack of data in the neighborhood of the missing values. We therefore suggest a hybrid of linear interpolation and graph machine learning for the imputation of tropospheric ozone time series.

Quality evaluation of ozone-processed Kinnow (Citrus reticulata Blanco) juice at ambient temperature

ABSTRACT Non-thermal processing has been developed to increase the quality and storage life of fruit juices in response to consumer desire. Ozonation is a recently developed method that generates volatile oxygen atoms that can eliminate a broad spectrum of microbes and extend the shelf stability of food products. The present research was intended to investigate the ozone processing on the physicochemical characteristics, microbiological quality, sensory attributes, and antioxidant potential of Kinnow juice. In this study, Kinnow juice was processed to 150 mg/h of ozone gas at a constant concentration for 5, 10, and 15 min. The sample was then taken in a plastic container and stored at 35°C ± 2 for three months. The results revealed that ozone processing had a significant (p˂0.05) effect on the total phenolics (TP) contents, total flavonoids (TF) contents, total soluble solids (TSS), acidity, total antioxidant activity (TAA) and DDPH radical scavenging activity of Kinnow juice. In addition, the ozonation application also significantly (p˂0.05) decreased the population of the microbes when the ozone time was increased from 5–15 min. Among the treatments, it was observed that T3 had the highest TSS, acidity, TF contents, TAA, and DDPH radical scavenging activity values and the lowest mold and plate counts. Regarding sensory qualities, T0+ showed the highest sensory score during storage. Thus, it may be concluded from the findings of our study that ozone application improved the quality of Kinnow juice by increasing TSS, phytochemicals and decreasing the microbial population, thereby prolonging their shelf life.

Effectiveness of Ozonation for Improving the Microbiological Safety of Fresh-Cut Parsley (Petroselinum crispum) Leaves

In the last decade, fresh-cut plants have become a more popular flavoring additive in food. It is important to find an effective method for ensuring the safety and quality of plant materials used as food additives. Ozonated water is being considered by the EFSA for approval as a cidal agent for plant protection. The objective of this study was to evaluate the effectiveness of ozonated water in improving the microbial safety of fresh-cut parsley leaves, with a particular focus on mesophilic and psychrotrophic bacteria and fungi. The yeasts and bacteria were identified with the MALDI-TOF MS system. Color changes on the surface of the parsley samples were measured in the CIE L*a*b trichromatic color model. The chemical composition of the essential oil was evaluated via gas chromatography with mass spectrometry (GCMS). The microbial level of the fresh leaves varied and depended on the season. The highest microbial levels were found in the leaves picked in the summer and autumn, at 104 to 106 CFU/g for fungi and 106 to 108 CFU/g for bacteria. Among the isolates with the highest isolation frequency, bacteria belonging to Pseudomonas fluorescens, Staphylococcus warneri, and Bacillus megaterium dominated. The dominant yeasts and molds were Candida sp., Rhodotorula sp., Cladosporium sp., and Fusarium sp. The conditions for water ozonation (ozone dose and time) were established for both mono- and mixed cultures. Time of 3 min, ozone content of 0.5, O3 mg/L or 1 min, and 1.5 mg of O3 mg/L were sufficient for a 90% reduction in the number of living microorganisms. Yeasts and bacteria were sensitive to ozone treatment, excluding P. fluorescens rods. The tested mold strains were the most resistant. However, it was noted that organic matter might reduce the decontamination effect. The effectiveness of ozonation was negatively influenced by organic compound content above 1%. Spectrophotometric measurements of parsley leaves after ozonation, especially after 3 min treatment at 1.5 O3 mg/L, revealed morphological changes. The CIELAB color space (L*a*b*) changed in the direction of lightness and yellowness; however, ΔE showed no statistically significant differences in comparison with the untreated leaves. In preliminary studies, no differences were noted in GLC-MS chromatograms for essential oils of parsley leaves before and after ozonation. The aroma of parsley treated with ozonated water was more intensely herbal than the control sample, probably due to the higher content of α and β phellandrene. The results of this study show that decontamination of parsley leaves by ozonated water containing 1.5 O3 mg/L in a closed 5 min process can effectively ensure the microbiological quality of fresh-cut parsley leaves. It can be concluded that ozone treatments in aqueous form appear to provide promising qualitative and quantitative results for the decontamination of this fresh-cut plant material. However, more work is necessary to study chemical and volatilome changes. Especially the sensory analyses should be conducted before and after ozone treatment.

Microwave radiometer observations of the ozone diurnal cycle and its short-term variability over Switzerland

Abstract. In Switzerland, two ground-based ozone microwave radiometers are operated in the vicinity of each other (ca. 40 km): the GROund-based Millimeter-wave Ozone Spectrometer (GROMOS) in Bern (Institute of Applied Physics) and the Stratospheric Ozone MOnitoring RAdiometer (SOMORA) in Payerne (MeteoSwiss). Recently, their calibration and retrieval algorithms have been fully harmonized, and updated time series are now available since 2009. Using these harmonized ozone time series, we investigate and cross-validate the strato–mesospheric ozone diurnal cycle derived from the two instruments and compare it with various model-based datasets: the dedicated GEOS-GMI Diurnal Ozone Climatology (GDOC) based on the Goddard Earth Observing System (GEOS-5) general circulation model, the Belgian Assimilation System for Chemical ObsErvations (BASCOE) – a chemical transport model driven by ERA5 dynamics, and a set of free-running simulations from the Whole Atmosphere Community Climate Model (WACCM). Overall, the two instruments show very similar ozone diurnal cycles at all seasons and pressure levels, and the models compare well with each other. There is a good agreement between the models and the measurements at most seasons and pressure levels, and the largest discrepancies can be explained by the limited vertical resolution of the microwave radiometers. However, as in a similar study over Mauna Loa, some discrepancies remain near the stratopause, at the transition region between ozone daytime accumulation and depletion. We report similar delays in the onset of the modelled ozone diurnal depletion in the lower mesosphere. Using the newly harmonized time series of GROMOS and SOMORA radiometers, we present the first observations of short-term (sub-monthly) ozone diurnal cycle variability at mid-latitudes. The short-term variability is observed in the upper stratosphere during wintertime, when the mean monthly cycle has a small amplitude and when the dynamics are more important. This is shown in the form of strong enhancements of the diurnal cycle, reaching up to 4–5 times the amplitude of the mean monthly cycle. We show that BASCOE is able to capture some of these events, and we present a case study of one such event following the minor sudden stratospheric warming of January 2015. Our analysis of this event supports the conclusions of a previous modelling study, attributing regional variability of the ozone diurnal cycle to regional anomalies in nitrogen oxide (NOx) concentrations. However, we also find periods with an enhanced diurnal cycle that do not show much change in NOx and where other processes might be dominant (e.g. atmospheric tides). Given its importance, we believe that the short-term variability of the ozone diurnal cycle should be further investigated over the globe, for instance using the BASCOE model.

Ozonation with Hydrogen Peroxide for Treating Wastewater from Industrial Potato Processing - a Preliminary Investigation

ABSTRACT Wastewater generated from agricultural activities and food production is a source of environmental pollution, and its treatment is a challenging technological issue. Ozone gas (O3), and the possibility of associating it with hydrogen peroxide (H2O2), has the potential to remove persistent organic compounds in effluents. In the present work, wastewater from industrial potato processing was characterized, and its treatment with ozone combined with hydrogen peroxide was assessed. The inlet ozone concentration and the flow rate were 84.5 mg L−1 and 62.5 mL min−1, respectively. The promising conditions of temperature, pH, and H2O2/O3 proportion were determined to reduce the chemical oxygen demand, turbidity, and color, according to a central composite rotatable design. The H2O2/O3 proportions were 0.1, 0.3, 0.5, and 0.6. Additionally, the response variables were evaluated as a function of the ozone and hydrogen peroxide treatment time for up to 24 h. The conditions obtained for treating wastewater from industrial potato processing were the following: temperature = 25°C; pH = 3.0, and H2O2/O3 proportion = 0.1. For the water quality parameters adopted, the combination of ozone and hydrogen peroxide was effective at removing color and turbidity. The chemical oxygen demand, color, and turbidity reduction stabilized after 1.16 h, with cumulative applied dosages of ozone and hydrogen peroxide equal to 380 mg L−1 and 35 mg L−1, respectively.

Hourly ozone level prediction based on the characterization of its periodic behavior via deep learning

Surface ozone has become one of the most important air pollutants in recent years, posing a huge threaten to human health. The accurate prediction of ozone pollution, especially when ozone concentration exceeds permissible standard, has become an important research topic in the field of air quality management, where data-driven methods are widely used. Among these methods, the long short-term memory neural network (LSTM) has received significant attention in ozone forecasting for its ability in modeling long-term time dependence. However, from the time series perspective, short-term nonstationary characteristics resulted from periodic trends of ozone data have not been sufficiently considered by LSTM. To address this issue, we propose a novel deep learning-based prediction method named multi-order difference embedded LSTM (MDELSTM). Through statistical analysis of ozone time series, a multi-order difference strategy is employed to extract the periodic information. By embedding it into LSTM layers, a brand-new deep learning prediction model with the ability to extract both long-term stationary information and short-term periodic information is constructed for ozone forecasting. The proposed method is verified through the Los Angeles air quality dataset between 2013 and 2017. The prediction accuracy of ozone concentration over the next hour is improved compared to other state-or-the-art methods in terms of mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). To further illustrate the ability of the proposed method in predicting high-level ozone concentration, new evaluation indicators oriented on situations where the ozone concentration exceeds permissible standard are proposed. The results demonstrate its application prospect in hourly ozone pollution prediction and prevention.

A Comparative Study of Deep Learning Models on Tropospheric Ozone Forecasting Using Feature Engineering Approach

This paper investigates the effect of the architectural design of deep learning models in combination with a feature engineering approach considering the temporal variation in the features in the case of tropospheric ozone forecasting. Although deep neural network models have shown successful results by extracting features automatically from raw data, their performance in the domain of air quality forecasting is influenced by different feature analysis approaches and model architectures. This paper proposes a simple but effective analysis of tropospheric ozone time series data that can reveal temporal phases of the ozone evolution process and assist neural network models to reflect these temporal variations. We demonstrate that addressing the ozone evolution phases when developing the model architecture improves the performance of deep neural network models. As a result, we evaluated our approach on the CNN model and showed that not only does it improve the performance of the CNN model, but also that the CNN model in combination with our approach boosts the performance of the other deep neural network models such as LSTM. The development of the CNN, LSTM-CNN, and CNN-LSTM models using the proposed approach improved the prediction performance of the models by 3.58%, 1.68%, and 3.37%, respectively.

Emprego de ozônio para melhorar a qualidade da palmeira Nipa (Nypa fruticans Wrumb.) e sua comercialização

ABSTRACT: The application of ozone as a treatment system for reducing microbial contaminant in Nipa bowls which are local products of the Palian river basin community, Trang province in Southern Thailand, was presented in this research. The ozone treatment system was designed and investigated for its performance to reduce microbial contaminant in nipa bowl products. Parameters affecting the performance of the system were optimized as well as ozone amount and treatment time. Under optimum condition (600 mg/h ozone and treatment time of 4 hours), the microbial decontamination was 4 log reduction, and the products could be stored for one month. The moisture content and the brightness of the ozone-treated products were significantly different from the untreated products when statistically tested at 95% confidence level. The moisture contents were 9.46 ± 0.10 and 10.54±0.31 %, and the brightness (L* value) were 74.93 ± 0.49 and 70.47 ± 0.65 for the ozone-treated and untreated products, respectively. Furthermore, the residual heavy metals were investigated, and no trace of metals was reported in nipa bowl samples. As a result, the nipa bowl products had met the standards as regards safety of food containers required by the Department of Medical Sciences Ministry of Public Health, Thailand. Following this collaboration between the university and the community, over 251,564 pieces of nipa bowls were sold which generated a total income of more than 84,000 USD to the Palian river basin community.