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The paper presents the treatment of atrazine-contaminated wastewater by ozonation followed by an anaerobic process using Upflow Anaerobic Sludge Blanket (UASB) reactor. The experiment was performed with 100 ppb synthetic solutions of atrazine prepared in ultra-pure water. The corresponding initial Chemical Oxygen Demand (COD) is 226 mg.L-1. The initial pH was adjusted to 9.5. The atrazine-bearing synthetic wastewater was ozonated with an ozone dose of 9.4mg/l for 40 minutes of optimum ozonation time, resulting in a 35% reduction in the initial concentration of atrazine. Along with atrazine reduction, there was a COD removal of 54.42%. Further, it was degraded with an anaerobic process, resulting in the final reduction in atrazine concentration of 81% and the corresponding removal in COD of 86.7%. The process of ozonation led to the mineralization of atrazine and enhancement in the biodegradability of the wastewater. Using ion chromatography, the ozonated wastewater sample was analyzed for ionic by-products before and after ozonation. The ion chromatography results showed the breaking of the atrazine compound and the formation of Cl-, NO3-, SO42-, and F- as intermediate products. Further, the BOD5/COD ratio increased, reflecting the increased biodegradability. This ozonated wastewater was treated in a UASB reactor where the pesticide was degraded to 19 ppb, and COs degraded to 30 mg.L-1. The overall removal of atrazine pesticide and COD were 81% and 86.7%, respectively, in the integrated system of ozonation followed by anaerobic degradation.

This paper forecasts the hourly AQI in Azamgarh, Uttar Pradesh, India, using machine learning (ML) models. This work involved the real-time measurement of hourly particulate matter (PM 2.5 , PM 10 ), gaseous concentrations (NO 2 , SO 2 ), and meteorological parameters (temperature, relative humidity, wind direction, wind speed, and uv radiation) in the ambient air. We have calculated the pollutant subindex and hourly AQI for Azamgarh from July 2022 to June 2023, encompassing a total of 8760 data points. The study observed annual average concentrations of NO 2 (33.91 ± 30.38 µg/m 3 ), SO 2 (43.96 ± 20.12 µg/m 3 ), PM 2.5 (52.25 ± 55.26 µg/m 3 ), and PM 10 (77.58 ± 80.61 µg/m 3 ). Meteorological conditions included a mean temperature of 27.20 ± 7.65 °C, relative humidity of 63.93 ± 18.74%, wind speed of 1.00 ± 0.96 m/s, and a uv index of 0.42 ± 0.34. The computed annual mean hourly AQI was 123 ± 91.96, which falls into the moderate-level pollution category. The AQI values in winter were higher than in summer. ANOVA analysis shows statistically significant monthly changes in pollutants and meteorological parameters: PM 2.5 (F = 842.7), PM 10 (F = 773.5), NO 2 (F = 583.6), SO 2 (F = 3356), temperature (F = 1045), humidity (F = 572.5), wind speed (F = 36.9), wind direction (F = 54.27), uv index (F = 24.48), and hourly AQI (F = 738.4), all with p < 2e-16. We predicted the hourly AQI using eight machine learning models: adaboost, catboost, gradient boosting, knn, linear regression, random forest, svm, and xgboost. With the fastest computational time of 1.61 s out of all the models examined, xgboost stood out as the best model because of its outstanding R 2 and RMSE performances. We employed a tenfold cross-validation to evaluate the models' performance. The results of the paired t-test indicate that catboost, gradboost, linear regression, and xgboost statistically align closely with the observed data. We can also observe the models' comparative performance using a taylor diagram. An investigation of sensitivity revealed that PM 2.5 , PM 10 , NO 2 , and SO 2 are crucial for the accuracy of the xgboost model in predicting AQI. Excluding these pollutants greatly reduces performance. In conclusion, machine learning can predict AQI, and xgboost is the best model for this purpose. Graphical Abstract Highlights Hourly gaseous, particulate and metrological components were measured (8760 data points). To forecast hourly AQI, we employed eight machine-learning algorithms. Annual mean AQI is 123 (moderate); higher in winter. XGboost stood out as the best models because of their outstanding R-squared, RMSE and least computational time (1.61 s). Cross-validation (tenfold) and Taylor diagram was employed to evaluate the models' performance. Sensitivity analysis reveals PM 2.5 , PM 10 , NO 2 , and SO 2 are crucial for XGboost's AQI predictions.

This paper forecasts the hourly AQI in Azamgarh, Uttar Pradesh, India, using deep learning (DL) models. In order to measure hourly particulate matter (PM 2.5 , PM 10 ), gaseous concentrations (NO 2 , SO 2 ), and meteorological parameters (temperature, relative humidity, wind direction, wind speed, and UV radiation), a total of 8760 data points were gathered between July 2022 and June 2023. The estimated annual mean hourly AQI was 123, indicating moderate pollution, with higher AQI values in the winter than in the summer. We used a MANOVA to ascertain the statistical significance of changes in PM 2.5 , PM 10 , SO 2 , and NO 2 at several time scales, including hourly, daily, weekly, and monthly. Every temporal scale examined by MANOVA showed significant differences in pollutants, with p  < 0.001 for hourly (Pillai's Trace = 0.149, F = 383.08), daily (Pillai's Trace = 0.772, F = 7418), weekly (Pillai's Trace = 0.396, F = 1433.1), and monthly (Pillai's Trace = 0.393, F = 1419.2). An ANOVA revealed that there were extremely significant changes every day (F = 170.7, p  < 0.001), every week (F = 2270, p  < 0.001), and every month (F = 2215, p  < 0.001) in addition to the considerable hourly variation (F = 8.612, p  = 0.00335). Moreover, the AQI varied significantly over the day and night, as shown by t-tests, with the nighttime mean (135) significantly higher than the daytime mean (111) (t = -11.906, p  < 0.001). The hourly AQI was predicted using six deep learning models: Transformer, Gated Recurrent Units (GRU), Convolutional Neural Network (CNN), Feedforward Neural Network (FNN), Long Short-Term Memory (LSTM), and Multi-Layer Perceptron (MLP). The FNN performed better than the other models, with the lowest MAE of 2.89, the lowest RMSE of 4.99, and the greatest R-squared value of 0.9971 with a reasonable processing time of 28 s. A Taylor diagram was used to show how well the models performed in comparison. Sensitivity analysis revealed that PM 2.5 , NO 2  and SO 2 have the greatest effects on FNN model forecasts. These findings suggest that FNN has the potential to significantly enhance AQI forecasts and be helpful in developing complex, fine-scale air pollution forecasting models.

There are a significant number of global and regional studies on air pollution prediction using machine learning. This study looks at the application of machine learning to anticipate air pollution, as well as the state of the field right now and its projected expansion. This study searches over 1794 documents created by 5354 academics and published in 745 publications between 1991 and 2023, using Scopus as the primary search engine. For the purpose of identifying and visualising major authors, journals, countries, research publications, and key trends on these concerns, articles published on these themes were evaluated using Biblioshiny, Vosviewer and S-curve analysis. We discover that interest in this subject began to grow in 2017 and has since grown at a rate of 18.56 percent per year. Although prestigious journals such as Environmental Pollution, Atmospheric Environment, and Science of the Total Environment have been at the forefront of advancing research on the application of machine learning to forecast air pollution, these journals are not the only ones doing so. The top four leading countries in terms of total citations are China (6,784 citations), the United Kingdom (2,758 citations), the United States (2145 citations), and India (1,117 citations). The top three most prestigious universities are Fudan University, China (63 articles), the University of Southern California, USA (60 articles), and Tsinghua University, China (56 articles). The authors' keyword co-occurrence network mappings show that machine learning (577 occurrences), air pollution (282 occurrences), and air quality (166 occurrences) are the top three most frequent keywords, respectively. This research focuses on using machine learning to predict air pollution.

The lower concentration of arsenic in the groundwater is serious health concerns of the people who are continuously taking from their drinking water. In this study, synthetic arsenic-contaminated water was prepared in the laboratory with varying concentrations of arsenic (100 to 1000 μg/L) and treated by nanosize adsorbent (copper oxide nanoparticles (CuO NPs)). The colloidal and powder form of CuO NPs were synthesized in the laboratory by the hydrothermal technique on a large scale and their shape and size were confirmed by XRD, FTIR, FESEM, and HRTEM analysis. It was found 30 ± 2 nm as size and spherical shape. The equilibrium adsorption of As (III) occurred at 90 min of contact time, pH 7.5, and 4 g/L adsorbent dosage. The maximum percent removal of As (III) was reached to 97.8, 94.6, 91.5, and 88.4% at an initial arsenic concentration of 100, 200, 500, and 1000 μg/L, respectively. The adsorption of As (III) followed pseudo-second-order kinetic and Freundlich isotherm model. Moreover, the overall cost of the synthesized CuO NPs (including material, operational, manpower, and transport cost with other overhead charges) was Rs. 281.832 g −1 , which is lesser than the market price (Rs. 500.018 g −1 ). Hence, the optimized adsorption design would help for the efficient removal of As (III) from aqueous medium.