Exposure to atmospheric pollution is a major concern for urban populations. Currently, no effective strategy has been adopted to tackle the problem. The paper presents the Smart Clean Air City project, a pilot experiment concerning the improvement in urban air quality. Small wet scrubber systems will be operating in a network configuration in suitable urban areas of L’Aquila city (Italy). The purpose of this work is to describe the project and show the preliminary results obtained in the characterization of two urban sites before the remediation test; the main operating principles of the wet scrubber system will be discussed, as well as the design of the mobile treatment plant for the processing of wastewater resulting from scrubber operation. Measurements of particle size distributions in the range of 0.30–25 µm took place in the two sites of interest, an urban background and a traffic area in the city of L’Aquila. The mean number concentration detected was 2.4 × 10⁷ and 4.5 × 10⁷ particles/m³, respectively. Finally, theoretical assessments, performed by Computational Fluid Dynamics (CFD) codes, will show the effects of the wet scrubber operation on air pollutants under different environmental conditions and in several urban usage patterns. Full article

Climate policy has a strong influence on policy processes at national levels in Indonesia, while other policies with a focus on air quality improvement are being implemented at local levels. Indonesia as a developing country has committed to reducing greenhouse gas (GHG) emissions by 29 percent by the year 2030. This calls into question the extent to which cities and local governments can cope with the challenges of climate change mitigation. The purpose of the research is to find out the extent to which local air pollution reduction policies can contribute to the climate change mitigation program. The research design involved an empirical case study on governance and policy relevant to climate change efforts to lower GHG in Bandung City, Indonesia. The study evaluated the air quality improvement and the climate change mitigation programs using the actor-based framework of the Contextual Interaction Theory (CIT). The governance and stakeholder characteristic of climate change mitigation were also analysed using the structural context part of the CIT framework. The result shows that air quality improvement policy is implemented separately from climate policy; the latter operates at the national level and the former at the local level. By looking at the actor interaction analysis, the study concludes that a more holistic environmental policy approach would be more efficient at reducing local air pollution and contributing to the mitigation of climate change. Full article

Adsorption of pollutants on activated carbon is an effective air pollution control technique. In this study, a strong and non-uniform electric field was applied over an activated carbon fiber cloth. The adsorption kinetic of several organic compounds (Acetone, Acetaldehyde, Benzene, Cyclohexane, Ethanol, Methyl Ethyl Ketone, Toluene, 1-Propanol) on the activated carbon cloth was evaluated in the presence and in the absence of an electric field. Results suggest that its application enhances the adsorptive process. A linear correlation was found between such enhancement and the specific heat of liquefaction of the organic compounds. Full article

Air quality assessment is an important task for local authorities due to several adverse health effects that are associated with exposure to e.g., urban particle concentrations throughout the world. Based on the consumption of costs and time related to the experimental works required for standardized measurements of particle concentration in the atmosphere, other methods such as modelling arise as integrative options, on condition that model performance reaches certain quality standards. This study presents an Artificial Neural Network (ANN) approach to predict atmospheric concentrations of particle mass considering particles with an aerodynamic diameter of 0.25–1 μm (PM(0.25–1)), 0.25–2.5 μm (PM(0.25–2.5)), 0.25–10 μm (PM(0.25–10)) as well as particle number concentrations of particles with an aerodynamic diameter of 0.25–2.5 μm (PNC(0.25–2.5)). ANN model input variables were defined using data of local sound measurements, concentrations of background particle transport and standard meteorological data. A methodology including input variable selection, data splitting and an evaluation of their performance is proposed. The ANN models were developed and tested by the use of a data set that was collected in a street canyon. The ANN models were applied furthermore to a research site featuring an inner-city park to test the ability of the approach to gather spatial information of aerosol concentrations. It was observed that ANN model predictions of PM(0.25–10) and PNC(0.25–2.5) within the street canyon case as well as predictions of PM(0.25–2.5), PM(0.25–10) and PNC(0.25–2.5) within the case study of the park area show good agreement to observations and meet quality standards proposed by the European Commission regarding mean value prediction. Results indicate that the ANN models proposed can be a fairly accurate tool for assessment in predicting particle concentrations not only in time but also in space. Full article

Prescribed rangeland burning in April is a long-standing practice in the Flint Hills region of eastern Kansas to maintain the tallgrass prairie ecosystem. The smoke plumes originating from these fires increases ambient PM_2.5 concentrations and potentially contributes to ozone (O₃) exceedances in downwind communities. Source apportionment research using Unmix modeling has been utilized to estimate contributions of Kansas rangeland burning to ambient PM_2.5 concentrations. The objective of this study was to investigate the potential correlations between O₃ and various sources of PM_2.5 that are derived from receptor modeling, and then to specifically estimate contributions of Kansas rangeland burning to ambient O₃ concentrations through regression analysis. Various daily meteorological data were used as predictor variables. Multiple regression models were developed for the eight-hour daily maximum O₃ as well as the daily contributions of the five PM_2.5 source categories that were derived from receptor modeling. Cross correlation was analyzed among residuals of the meteorological regression models for O₃ and the daily contributions of the five PM_2.5 source categories in order to identify the potential hidden correlation between O₃ and PM_2.5. The model including effects of meteorological variables and episodic contributions from fire and industrial emissions can explain up to 78% of O₃ variability. For non-rainy days in April, the daily average contribution from prescribed rangeland burning to O₃ was 1.8 ppb. On 3% of the days in April, prescribed rangeland burning contributed over 12.7 ppb to O₃; and on 7% of the days in April, burning contributed more than 7.2 ppb to O₃. When the intensive burning activities occur in days with high O₃ background due to high solar radiation or O₃ carryover from the previous day, the contributions from these episodic fire emissions could result in O₃ exceedances of the National Ambient Air Quality Standards (NAAQS). The regression models developed in this study demonstrated that the most valuable predictors for O₃ in the Flint Hills region include the O₃ level on the previous day, total solar radiation, difference between daily maximum and minimum air temperature, and levels of episodic fire and industrial emissions. The long term goal is to establish an online O₃ forecasting tool that can assist regulators and land managers in smoke management during the burning season so that the intensive burning activities can be planned to avoid forecasted high O₃ days and thus prevent O₃ exceedance. Full article