ABSTRACT
Industrial wastewaters are different from sanitary wastewaters, and treatment complications due to their unique characteristics, so biological processes are typically disrupted. High chemical oxygen demand, dye, heavy metals, toxic organic and non-biodegradable compounds present in petroleum industry wastewater. This study intends to optimize the photocatalytic proxone process, utilizing a synthesized ZnO-Fe3O4 nanocatalyst, for petroleum wastewater treatment. The synthesis of ZnO-Fe3O4 was done by air oxidation and layer-by-layer self-assembly method and XRD, SEM, EDAX, FT-IR, BET, DRS, and VSM techniques were used to characterize the catalyst. Central composite design (CCD) method applied to investigated the effect of pH (4-8), reaction time (30-60 min), ozone gas concentration (1-2 mg/L-min), hydrogen peroxide concentration (2-3 mL/L) and the amount of catalyst (1-0.5 g/L) on the process. In the optimal conditions, biological oxygen demand (BOD5) and total petroleum hydrocarbon (TPH) removal, reaction kinetic, and synergistic effect mechanisms on the process were studied. Based on the ANOVA, a quadratic model with R2 = 0.99, P-Value = 0.0001, and F-Value = 906.87 was proposed to model the process. Based on the model pH = 5.7, ozone concentration = 1.8 mg/L-min, hydrogen peroxide concentration = 2.5 mL/L, reaction time = 56 min, and the catalyst dose = 0.7 g/L were proposed as the optimum condition. According to the model prediction, an efficiency of 85.3% was predicted for the removal of COD. To evaluate the accuracy of the prediction, an experiment was carried out in optimal conditions, and experimentally, a 52% removal efficiency was obtained. Also, at the optimum condition, BOD5 and TPH removal were 91.1% and 89.7% respectively. The reaction kinetic follows the pseudo-first-order kinetic model (R2 = 0.98). Also, the results showed that there is a synergistic effect in this process. As an advanced hybrid oxidation process, the photocatalytic proxone process has the capacity to treat petroleum wastewater to an acceptable standard.
ABSTRACT
Purpose: The association between air pollutant (PM2.5, PM10, NO2, and O3) concentrations and daily number of COVID-19 confirmed cases and related deaths were evaluated in three major Iranian cities (Tehran, Mashhad, and Tabriz). Methods: Hourly concentrations of air pollutants and daily number of PCR-confirmed cases and deaths of COVID-19 were acquired (February 20th, 2020 to January 4th, 2021). A generalized additive model (GAM) assuming a quasi-Poisson distribution was used to model the associations in each city up to lag-day 7 (for mortality) and 14 (for morbidity). Then, the city-specific estimates were meta-analyzed using a fixed effect model to obtain the overall relative risks (RRs). Results: A total of 114,964 confirmed cases and 21,549 deaths were recorded in these cities. For confirmed cases, exposure to PM2.5, NO2, and O3 for several lag-days showed significant associations. In case of mortality, meta-analysis estimated that the RRs for PM2.5, PM10, NO2, and O3 concentrations were 1.06 (95% CI: 0.99, 1.13), 1.06 (95% CI: 0.93, 1.19), 1.15 (95% CI: 0.93, 1.38), and 1.07 (95% CI: 0.84, 1.31), respectively. Despite several positive associations with all air pollutants over multiple lag-days, COVID-19 mortality was only significantly associated with NO2 on lag-days 0-1 and 1 with the RRs of 1.35 (95% CI: 1.04, 1.67) and 1.16 (95% CI: 1.02, 1.31), respectively. Conclusion: This study showed that air pollution can be a factor exacerbating COVID-19 infection and clinical outcomes. Actions should be taken to reduce the exposure of the public and particularly patients to ambient air pollutants. Supplementary Information: The online version contains supplementary material available at 10.1007/s40201-021-00736-4.
