Non-thermal plasma (NTP) is a promising technology for the improvement of indoor air quality (IAQ) by removing volatile organic compounds (VOCs) through advanced oxidation process (AOP). In this paper, authors developed a laboratory scale dielectric barrier discharge (DBD) reactor which generates atmospheric NTP to study the removal of low-concentration formaldehyde (HCHO), a typical indoor air VOC in the built environment associated with cancer and leukemia, under different processing conditions. Strong ionization NTP was generated between the DBD electrodes by a pulse power zero-voltage switching flyback transformer (ZVS-FBT), which caused ionization of air molecules leading to active species formation to convert HCHO into carbon dioxide (CO₂) and water vapor (H₂O). The impact of key electrical and physical processing parameters i.e. discharge power (P), initial concentration (C_in), flow rate (F), and relative humidity (RH) which affect the formaldehyde removal efficiency (ɳ) were studied to determine optimum conditions. Results show that, the correlation coefficient (R²) of removal efficiency dependence on the processing parameters follow the order R² (F) = 0.99 > R² (RH) = 0.96, > R² (C_in) = 0.94 > R² (P) = 0.93. The removal efficiency reached 99% under the optimum conditions of P = 0.6 W, C_in = 0.1 ppm, F = 0.2 m³/h, and RH = 65% with no secondary pollution. The study provided a theoretical and experimental basis for the application of DBD plasma for air purification in the built environment.

Nitrogen dioxide (NO₂) is a critical air pollutant and key indicator for air quality. Due to limited monitoring, we leveraged TROPOMI NO₂ and NASA POWER meteorological datasets to evaluate the meteorological drivers on NO₂ tropospheric column concentrations and to develop predictive models for NO₂ levels over Ghana. Employing an 8:2 ratio for model training and testing, NO₂ and meteorology relationships were assessed by seasonality indices and correlation analyses. Results indicate marked seasonal variability in NO₂ columns, prominent during the dry season. Wind speed, relative humidity, and precipitation significantly reduce NO₂, whereas temperature correlated positively in the southern forested zone. Predictive models demonstrate varying efficacy across climatic zones, with mean percentage differences ranging 9.87 to 37.76% and agreement index up to 0.96. The Random Forest and XGBoost models showed outstanding performance, with correlation reaching 0.92. This results presents a scalable methodology for NO₂ monitoring providing insights for air quality management.

This paper highlights some of the commonly used bio-based materials studied for their applicability as adsorbents in wastewater treatment. Additionally, few processing techniques employed to enhance the ability and or affinity of the adsorbents for wastewater treatment have been discussed. More so, some of the commonly used characterization techniques such as Scanning Electron Microscopy (SEM), Fourier Transform InfraRed (FTIR) spectroscopy among others often employed in a bid to elucidate the properties and morphologies of the adsorbents as well as the potential mechanism(s) underlying the adsorbate-adsorbent interaction(s) has also been extensively conferred. The potential draw-backs, recommendations and future perspectives on the use of bio-based materials as adsorbents in wastewater treatment has also been discussed in the concluding section of this paper.

Nitrobenzene (NB) is toxic and carcinogenic aromatic compound widely used in several industries which is ultimately found in their effluents. In this work, dielectric barrier discharge (DBD) reactor was employed for the degradation of nitrobenzene in aqueous solution. Active species like O₃ and ^•OH produced by DBD reactor were mixed with water which degraded the NB. The results indicated that the lower NB concentrations slightly acidic conditions and high voltage ranges showed the optimum efficiencies. Moreover, the impacts of active species inhibitors isopropyl alcohol (IPA), tert-butanol (TBA), inorganic ions for instance sulfates ( ${\mathrm{S}\mathrm{O}}_4^{2-}$), bicarbonates ( ${\mathrm{H}\mathrm{C}\mathrm{O}}_3^{-}$), nitrates ( ${\mathrm{N}\mathrm{O}}_3^{-}$), carbonates ( ${\mathrm{C}\mathrm{O}}_3^{2-}$) and chlorides (Cl⁻) on the degradation of NB were examined. This analysis showed that the hydroxyl radical was captured by the addition of these inhibitors and resulted in the decrease in efficiencies. Byproducts produced during the degradation of nitrobenzene were assessed by analytical techniques of high-performance liquid chromatography (HPLC), liquid chromatography–mass spectrometry (LC–MS), UV–visible spectroscopy and total organic carbon (TOC) analysis. Main intermediate products were nitrophenols and low molecular weight organic acids including oxalic acid and acetic acid that were eventually mineralized to CO₂ and H₂O. The dielectric barrier discharge technology was found productive for the degradation of nitroaromatic compounds. GRAPHICAL ABSTRACT

Clouds play a crucial role in Earth’s climate system by modulating radiation fluxes via reflection and scattering, and thus the slightest variation in their spatial coverage significantly alters the climate response. Until now, due to the sparse distribution of advanced observation stations, large uncertainties in cloud climatology remain for many regions. Therefore, this paper estimates total cloud cover (TCC) by using sunshine duration measured in different tropical climates in Ghana. We used regression tests for each climate zone, coupled with bias correction by cumulative distribution function (CDF) matching, to develop the estimated TCC dataset from nonlinear empirical equations. It was found that the estimated percentage TCC, 20.8–84.7 ± 3.5%, compared well with station-observed TCC, 21.9–84.4 ± 3.5%, with root mean square errors of 1.08–9.13 ± 1.8% and correlation coefficients of 0.87–0.99 ± 0.03. Overall, spatiotemporal characteristics were preserved, establishing that denser clouds tended to prevail mostly over the southern half of the forest-type climate during the June–September period. Moreover, the model and the observations show a non-normality, indicating a prevalence of above-average TCC over the study area. The results are useful for weather prediction and application in meteorology.