Journal of Environmental Engineering

Subsurface-flow constructed wetlands (SSF CWs), which include horizontal subsurface–flow constructed wetlands (HSSF CWs) and vertical subsurface–flow constructed wetlands (VSSF CWs), have become increasingly popular for the treatment of various waste streams such as wastewater, landfill leachate, and stormwater runoff. These wetlands utilize a variety of natural physical, chemical, and microbial processes to effectively remove contaminants. This article aims to provide valuable insights into the application of subsurface-flow wetlands for the treatment of stormwater runoff by collecting and reviewing relevant articles. Within the realm of stormwater contaminants, this review primarily focuses on nutrients, heavy metal, and per- and polyfluoroalkyl substances (PFAS), which are commonly found in stormwater runoff worldwide. The removal percentages of these contaminants vary across different studies, depending on factors such as the characteristics of the contaminants, the type and scale of the subsurface-flow wetland, as well as the selection of plants and substrates used. This study provides valuable insights into the various processes that significantly contribute to the removal of contaminants, including adsorption, plant uptake, and biodegradation. The removal percentage of PFAS ranges from 1% to 99% in constructed wetlands. The wide range of PFAS removal comes from differences between PFAS category, plant and soil type, wetland configuration, and matrix effects. Additionally, this paper brings attention to the current gaps and limitations present in the research conducted within this field. It is imperative to conduct further studies utilizing real-world or synthetic stormwater runoff to deepen our comprehension in this domain. Furthermore, considering that each category of PFAS possesses unique properties, conducting individual studies on each group becomes crucial to attain a comprehensive understanding of the behavior and transformation of PFAS in CWs.

Nitrogen oxide emissions have attracted much attention due to their impact on the atmospheric environment and human health. Combined with NO removal in the cement production process, coal gasification slag was used to prepare denitrification materials to decompose NO in cement kilns without the addition of reducing agents. Denitrification materials were synthesized by a chemical precipitation method at different pH. The denitrification mechanism was proposed in this study. The results indicated that the denitrification material prepared at a pH of 7 exhibited the best denitrification performance. The NO decomposition rate first increased and then decreased below 400°C, and then increased as the temperature increased, reaching over 97% above 700°C. In the low-temperature range, Fe(II) was assumed to be active species, and the absorbed NO on the Fe(II) site obtained an electron, which promoted the decomposition of NO. In the high-temperature range, besides the active element Fe, the residual carbon in the coal gasification slag was prone to oxidation-reduction reactions, reducing nitric oxide to nitrogen. A new waste-to-value concept is introduced in this study, which promotes green technology for sustainable development.

This study focuses primarily on sulfur dioxide (${\mathrm{SO}}_2$) emissions control problem in a wet flue gas desulfurization (WFGD) process, and our objective is to design an intelligent control system so that the outlet ${\mathrm{SO}}_2$ concentration satisfies the ${\mathrm{SO}}_2$ emission standard. In our approach, a multimodel control framework, which is made up of a linear robust controller and a neural controller, is integrated with the invasive weed optimization (IWO) algorithm in an elegant fashion and used for ${\mathrm{SO}}_2$ emissions control purposes. A case study is carried out based on operation data from a 600 MW coal-fired unit, and simulation results show that IWO-based automatic clustering can identify different operating modes in the WFGD process with high accuracy. Further, the established multimodel control system can remove ${\mathrm{SO}}_2$ emissions effectively. Experimental results show that ${\mathrm{SO}}_2$ emissions can be removed effectively with the proposed method, and this could provide engineering guidance to design a WFGD control system.

The present study was performed to explore the bactericidal effect of ozone micronano bubbles (OMNBs) against Pseudomonas aeruginosa, Escherichia coli (as gram-negative bacteria), Enterococcus faecalis, and Staphylococcus aureus (gram-positive bacteria). The effects of conventional ozonation (CO) or macrobubbles were also investigated for comparison. Three rates of ozone (10, 5, and $1.7\mathrm{g}/\mathrm{h}$) in both forms (OMNB and CO) were employed in different time contacts. Samples were collected at different contact times. The most significant bactericidal effect of OMNB was observed in P. aeruginosa and S. aureus at $5\mathrm{g}/\mathrm{h}$ ozone, which killed 100% of these bacteria after 8 and 5 min, respectively, whereas the kill times by CO at the same rates were 120 min for P. aeruginosa and 90 min for S. aureus, E. coli, and E. faecalis showed the same pattern of kill time, 60 and 60 min by CO and 3 and 2 min by OMNB, respectively. Results showed that the removal efficiency was significantly increased compared to the ordinary ozonation at the same ozone rates. According to the results of this study, disinfection of water contaminated with these bacteria can be effectively performed using OMNBs to accelerate and increase the efficiency of the disinfection process. The results may open a new horizon to disinfect medical and food processing instruments and surfaces more effectively without remaining residuals and harm to the environment.

The inhalation of particulate matter (PM) has been linked to various respiratory illnesses, including asthma, chronic obstructive pulmonary disease, and cancer. The COVID-19 pandemic has heightened concerns about PM as a possible route of virus transmission through airborne particles. This study investigated the impact of lockdown on particulate matter and mass-segregated PM (${\mathrm{PM}}_{2.5}$ and ${\mathrm{PM}}_{10}$) deposition in human lungs. In this study, the daily average concentration of particulate matter (${\mathrm{PM}}_{2.5}$ and ${\mathrm{PM}}_{10}$) was observed from March 22 to May 15, 2021, and grouped into two categories: before and during lockdown. The lockdown resulted in a 1.6% decrease in ${\mathrm{PM}}_{2.5}$ concentration and a 15% decrease in ${\mathrm{PM}}_{10}$ concentration. Ground-level particulate matter concentrations were found to be positively correlated with daily new COVID-19 cases and daily deaths during the lockdown period. Meteorological temperature was found to be positively associated with daily cases and daily deaths due to COVID-19. The noncarcinogenic risk was calculated by hazard quotient (HQ) with annual daily dose (ADD), and it exceeded the target value of 1 in almost all cases. Analysis of airway geometry and mass deposition indicated that mass deposition was reduced by 25% and 13% for ${\mathrm{PM}}_{2.5}$ and ${\mathrm{PM}}_{10}$, respectively. Therefore, ambient air pollution may be considered to be a major cofactor for the high death rate and number of daily cases during the pandemic. Based on the obtained results, the study recommends implementing effective pollution mitigation strategies to reduce the risk of respiratory illnesses and disease transmission through the air as a medium.