Air Pollution Control Research Articles

Probing the capacity of a spatiotemporal deep learning model for short-term PM2.5 forecasts in a coastal urban area

Accurate forecast of fine particulate matter (PM2.5) is crucial for city air pollution control, yet remains challenging due to the complex urban atmospheric chemical and physical processes. Recently deep learning has been routinely applied for better urban PM2.5 forecasts. However, their capacity to represent the spatiotemporal urban atmospheric processes remains underexplored, especially compared with traditional approaches such as chemistry-transport models (CTMs) and shallow statistical methods other than deep learning. Here we probe such urban-scale representation capacity of a spatiotemporal deep learning (STDL) model for 24-hour short-term PM2.5 forecasts at six urban stations in Rizhao, a coastal city in China. Compared with two operational CTMs and three statistical models, the STDL model shows its superiority with improvements in all five evaluation metrics, notably in root mean square error (RMSE) for forecasts at lead times within 12 h with reductions of 49.8 % and 47.8 % respectively. This demonstrates the STDL model's capacity to represent nonlinear small-scale phenomena such as street-level emissions and urban meteorology that are in general not well represented in either CTMs or shallow statistical models. This gain of small-scale representation in forecast performance decreases at increasing lead times, leading to similar RMSEs to the statistical methods (linear shallow representations) at about 12 h and to the CTMs (mesoscale representations) at 24 h. The STDL model performs especially well in winter, when complex urban physical and chemical processes dominate the frequent severe air pollution, and in moisture conditions fostering hygroscopic growth of particles. The DL-based PM2.5 forecasts align with observed trends under various humidity and wind conditions. Such investigation into the potential and limitations of deep learning representation for urban PM2.5 forecasting could hopefully inspire further fusion of distinct representations from CTMs and deep networks to break the conventional limits of short-term PM2.5 forecasts.

The screening evaluation of environmental odors: a new dispersion modelling-based tool.

Odor pollution is the biggest source of complaints from citizens concerning environmental issues after noise. Often, the need for corrective actions is evaluated through simulations performed with atmospheric dispersion models. To save resources, air pollution control institutions perform a first-level odor impact assessment, for screening purposes. This is often based on Gaussian dispersion models (GDM), which does not need high computational power. However, their outputs tend to be conservative regarding the analyzed situation, rather than representative of the real in-site conditions. Hence, regulations and guidelines adopted at an institutional level for authorization/control purposes are based on Lagrangian particle dispersion models (LPDM). These models grant a more accurate simulation of the pollutants' dispersion even if they are more demanding regarding both technical skills and computing power. The present study aims to increase the accuracy of screening odor impact assessment by identifying the correlation function of the outputs derived from the two simulation models. The case study is placed in northern Italy, where a single-point source, with various stack heights, was considered. The case study is placed in northern Italy, where a single-point source, with various stack heights, was considered. The obtained correlation functions allow the practitioner to have a more accurate first-level odor impact assessment, to save time for training, and to reduce the site-specific meteorological data before proceeding with the simulation. The identified functions could allow institutions to estimate the results that would have been forecasted with the application of the more complex LPDM, applying, however, the much simpler GDM. This solution grants an accurate tool which can be used to address citizens' concerns while saving workforce and technical resources. Limitations are related to the specificity of the method regarding type sources, orography, and meteorological conditions. Comparison with other screening tools is also presented and discussed.

Assessment of the status of PM2.5 and ground-level O3 pollution variations and meteorological factor effects: A case study in Long An Province, Vietnam

Abstract Long An Province has a favorable geographical position in socio-economic development exchanges and is a fundamental bridge between the area of Ho Chi Minh City (HCMC) and the Southeast localities with the Southwestern provinces. Along with the master plan for socio-economic development with a vision for 2030, the province’s air quality issues, especially PM2.5 and surface O3 pollution, will be complex and pose many challenges to effective air pollution management and control. This study aims to investigate the effects of major meteorological characteristics (temperature–T, relative humidity–RH, surface pressure–P, and wind speed–WS) on temporal changes in the correlation between PM2.5 and surface O3 concentrations in Long An province in the 2018-2020 period (computed by a system of coupled WRF/CMAQ models) through trend evaluation using by Mann-Kendall (MK) Test and Theil-Sen Slope, and Pearson correlation analysis. The study results reported that in terms of temporal variations, both value levels of PM2.5 and O3 in the dry season tended to surpass that in the rainy season. Moreover, PM2.5 levels tended to exceed the 24-hour mean threshold (between 1.001 and 3.14 times) and the annual mean threshold (from 1.81 to 2.03 times) of the Vietnamese National Ambient Air Quality Standard. Based on the Pearson correlation analysis, significant influences of meteorological conditions on the pollution status of PM2.5 and surface O3 were also demonstrated, such as the different positive and negative correlations of each meteorological factor on concentration. These outstanding results serve as an essential reference for developing air quality targets and proposing future air pollutant control measures in Long An Province.

Reduction in polycyclic aromatic hydrocarbon exposure in Beijing following China’s clean air actions

Exposure to polycyclic aromatic hydrocarbons (PAHs) in the Chinese population was among the highest globally and associated with various adverse effects. This study examines the impact of China’s two-phase clean air initiatives, namely the Air Pollution Prevention and Control Action Plan (APPCAP) in 2013–2017 and the Blue-Sky Defense War (BSDW) in 2018–2020, on PAH levels and human exposures in Beijing. To evaluate the effects of APPCAP, we measured 16 PAHs in 287 PM2.5 samples collected in Beijing and 9 PAH metabolites in 358 urine samples obtained from 54 individuals who traveled from Los Angeles to Beijing between 2014 and 2018. The concentration of PM2.5-bound benzo[a]pyrene equivalents (BaPeq) decreased by 88.5% in 2014–2018 due to reduced traffic, coal, and biomass emissions. PAH metabolite concentrations in travelers’ urine decreased by 52.3% in Beijing, correlated with changes in PM2.5 and NO2 levels. In contrast, no significant changes were observed in Los Angeles. To evaluate BSDW’s effects, we collected 123 additional PM2.5 samples for PAH measurements in 2019–2021. We observed sustained reductions in BaPeq concentrations attributable to reductions in coal and biomass emissions during the BSDW phase, but those from traffic sources remained unchanged. After accounting for meteorological factors, China’s two-phase clean air initiatives jointly reduced Beijing’s PM2.5-bound BaPeq concentrations by 96.6% from 2014 to 2021. These findings provide compelling evidence for the effectiveness of China’s clean air actions in mitigating population exposure to PAHs in Beijing.

Numerical study on the efficiency of an electrostatic precipitator having the shape of a truncated cone

This paper presents a comprehensive numerical study of a truncated conical precipitator. The main objective was to enhance the efficiency of the precipitator by exploring the influence of several parameters on particle trajectories and the evolution of the collection efficiency. The studied parameters include the cone coefficient ( D), flow velocity, applied voltage, conduit diameter and length, as well as relative permeability. For each parameter, analyses were conducted on the evolution of the collection efficiency for particles with various diameters, ranging from 0.01 to 10 μm. The results obtained from the numerical simulation on COMSOL Multiphysics® indicate that, regardless of the value of D, the precipitator exhibits optimal efficiency in collecting particles with extreme diameters (0.01 and 10 μm) due to the dominance of the electrical force. In contrast, particles with intermediate diameters (0.1–1 μm) present a challenge, as the drag and electric forces are too weak to ensure effective particle collection. The study highlights that a sharper tip at the top of the precipitator significantly enhances its efficiency. Increasing the applied voltage and selecting lower inner radii of the collecting electrode reinforce the electrical force and enhance particle collection. Furthermore, increasing the height of the precipitator directs particle trajectories more effectively toward the collecting electrode. The results provide valuable insights for the design of more efficient precipitators and propose practical guidelines for improving their effectiveness. These contributions are particularly important for air pollution control technologies, offering significant advancements in this field.

Multiphase flow simulation and industrial application of an in-line gasifier for NOx emissions control from cement kiln

Following the power and steel industries, nitrogen oxides (NOx) in the cement industry have become an important part of the next stage of air pollutant control. Consequently, the multiphase flow simulation of an in-line coal gasification denitration system was carried out using the computational fluid dynamics (CFD) approach. Among them, the solid phase is simulated by the Multiphase Particle-In-Cell (MP-PIC) method, and the gas phase turbulence is accurately captured by the large eddy simulation (LES) method. The flow field characteristics distribution and the reaction kinetic of NO under different parameters (coal, raw meal, tertiary air) in the gasifier were investigated, and the operating condition values for guiding industrial applications are obtained. The simulation results show that the gasifier can completely remove NOx from the rotary kiln at 2.8 kg/s for coal, 30 kg/s for raw meal and 2.5 kg/s for tertiary air. The application results show that under 1.6 kg/t.cl of ammonia water combined with SNCR, NOx emission and ammonia slip can be controlled below 50 mg/Nm3 and 5 mg/Nm3, respectively. Compared to similar denitrification technologies, this technology shows the potential to achieve ultra-low NOx emission levels in the cement industry.

Study on the establishment of air pollutant and carbon emission inventory and collaborative emission reduction potential of China's coking industry from 2012 to 2022

Coking industry is usually regarded as a high pollution and high energy consumption industry. China is accelerating its efforts to reduce pollution and carbon emissions in the industrial sector, which has received little attention as the world's largest producer of coke. Therefore, in this study, the trend of air pollution and carbon emissions in China's coking industry and the path of coordinated emission reduction were studied. The results indicate that the average annual emissions of PM, SO2, NOx, VOCs, and CO2 in China's coking industry from 2012 to 2022 amount to 205.98, 69.47, 193.45, 599.80 Gg and 191.10 Tg, respectively. The main sources of PM, SO2, NOx, VOCs and CO2 in coking industry were coal preparation (51.5 %), charge and pushing (39.5 %), coke oven gas (99.8 %), byproduct recovery (47.0 %) and fuel combustion (87.5 %). The emissions from coking plants in central and southern Shanxi, eastern and southern Hebei, and central Shandong are the most concentrated. Ultra-low emission transformation and deep treatment of VOCs have greatly reduced pollutant emissions in key areas of air pollutant control, but the actual emission reduction effect of these measures has been weakened by the additional emissions caused by the increase of coke production in other non-key areas. The research on synergetic emission reduction path shows that there is a great synergistic benefit between air pollutants and CO2 emission reduction in coking industry. It is estimated that the APeq (air pollutants and carbon equivalent) of China's coking industry in 2025, 2028 and 2030 will decrease by 38.2 %, 63.5 % and 70.8 % respectively compared with 2022. With the continuous promotion of pollution reduction and carbon reduction measures, the emission reduction potential of China's coking industry will gradually shift from key areas to non-key areas.

Assessing the nonlinearity of wintertime PM2.5 formation in response to precursor emission changes in North China with the adjoint method

Abstract While China’s clean air actions implemented since 2013 have been effective in mitigating PM2.5 air pollution, the large emission reductions during the COVID-19 lockdown period in early 2020 did not similarly alleviate PM2.5 pollution in North China, reflecting a distinct nonlinear chemical response of PM2.5 formation to emission changes. Here we apply emission-concentration relationships for PM2.5 diagnosed using the adjoint approach to quantitatively assess how chemical nonlinearity affects PM2.5 over Beijing in February 2020 in response to two emission reduction scenarios: the COVID-19 lockdown and 2013–2017 emission controls. We find that, in the absence of chemical nonlinearity, the COVID-19 lockdown would decrease PM2.5 in Beijing by 17.9 μg m–3, and the 2013–2017 emission controls resulted in a larger decrease of 54.2 μg m–3 because of greater reductions of SO2 and primary aerosol emissions. Chemical nonlinearity offset the decrease for Beijing PM2.5 by 3.4 μg m–3 during the lockdown due to enhanced sensitivity of aerosol nitrate to NO x emissions, but enhanced the efficiency of 2013–2017 emission controls by 11.9 μg m–3 due to the weakened heterogeneous reaction of sulfate. Such nonlinear chemical effects are important to estimate and consider when designing or assessing air pollution control strategies.

Analysis of spatial variability of smog episodes over National Capital Delhi during (2013–2017)

Air pollution is a pressing issue in Delhi, with smog occurrences causing reduced visibility and various respiratory problems. A series of severe SMOG (smoke + fog) episodes between 2013 and 2017 with reduced visibility and exceptionally high PM2.5 concentrations have been reported in Delhi especially around Diwali festival (October–November). The Smog of 2016 is referred as Great Smog of Delhi. This study examined remote sensing data from 2013 to 2017 to investigate smog episodes in Delhi during pre-Diwali, post-Diwali, and Diwali. Satellite-derived parameters viz absorbing aerosol index (AAI), aerosol optical depth (AOD), and ozone monitoring instrument (OMI) along with air pollution data and climatic parameters were used to analyze smog episodes. The results showed that during smog episodes, AOD, AAI and PM2.5 concentrations exceeded permissible limits significantly at all stations across Delhi during the Diwali festival. The ground-based observations at different locations across Delhi and satellite data-derived datasets confirmed the severity of smog episodes. The findings indicate that burning of fire crackers coupled with agriculture stubble burning and subsequent transport of the smoke from North Western states through the Capital had a greater impact on deteriorating air quality in Delhi than local pollution, especially during unfavorable weather conditions associated with high humidity and weaker winds. The outcomes highlight the significance of remotely sensed information in identifying smog episodes and their severity in Delhi. It also underlines the necessity for efficient interventions to control air pollution, particularly amid festivals like Diwali.

The impact of hydrogen fuel cell heavy-duty trucks purchase subsidies on air quality.

The pollutant emissions of diesel-powered heavy-duty trucks (HDTs) seriously damage the air quality. The promotion of hydrogen fuel cell HDTs through purchase subsidy policy to reduce emissions has become an important approach to control air pollution. This study focuses on the impact of hydrogen fuel cell HDT purchase subsidies on air quality in the context of China, covering the panel data of 31 Chinese cities from 2014 to 2021 and applying a two-way fixed effects model to analyze the contribution of purchase subsidies and hydrogen refueling station construction subsidies to air quality. Results show that (1) the increase in purchase subsidies could improve the air quality by around 6.1% and there is a lag effect. (2) Purchase subsidies make a larger contribution to air quality compared with construction subsidies. (3) Purchase subsidies can improve air quality by reducing carbon emissions in transport industry. In sight of these results, policy makers should emphasize the implementation of purchase subsidies and hydrogen refueling station construction subsidies and stimulate manufacturers to improve the performance of hydrogen fuel cell so as to contribute more to the environment.

Pemodelan Spasial Pengaruh Ruang Terbuka Hijau (RTH) Dengan Mengukur Tingkat Kadar Gas (Karbon Monoksida) (Studi Kasus: : Kelurahan Entrop Distrik Jayapura Selatan)

This research aims to model the impact of Green Open Space (GOS) on Carbon Monoxide (CO) gas levels in Entrop Village, South Jayapura District. Green Open Space plays a crucial role in improving air quality in an area. In the context of rapid urbanization and increasing motor vehicle numbers, GOS serves as the city's lungs, capable of absorbing pollutants and improving air quality. The research method used is spatial modeling, which allows mapping the distribution of GOS and measuring CO levels on the main roads of Entrop Village. Primary data was obtained through field surveys using CO measuring instruments, while secondary data was obtained from various literature sources and reports from related agencies. Data analysis was conducted using statistical and spatial approaches to identify the relationship between GOS and CO levels. The results showed a significant negative relationship between the extent of GOS and CO levels. The larger the GOS area, the lower the CO concentration in the area. Spatial modeling also identified high CO concentration points in areas with low GOS density. Furthermore, this research found that uneven distribution of GOS contributes to variations in air quality (CO) in different locations. These findings highlight the importance of effective GOS management as part of air pollution control strategies in large cities. Recommendations from this research include increasing the number and distribution of GOS in areas with high CO concentrations, and integrating GOS into urban spatial planning to create a healthier and more sustainable environment. This research makes a significant contribution to sustainable urban planning by emphasizing the importance of integrating GOS into spatial planning to improve air quality and environmental health. Thus, the results of this research can serve as a reference for the government and stakeholders in formulating future GOS management and air pollution control policies.

Effect of surfactant type and concentration on the gas-liquid mass transfer in biotrickling filters used for air pollution control

Volatile organic compounds (VOCs) emitted into the atmosphere negatively affect the environment and human health. Biotrickling filtration, an effective technology for treating VOC-laden waste gases, faces challenges in removing hydrophobic VOCs due to their low water solubility and therefore limited bioavailability to microorganisms. Consequently, the addition of (bio)surfactants has proven to be a promising strategy to enhance the removal of hydrophobic VOCs in biotrickling filters (BTFs). Yet, up to now, no single study has ever performed a mass transfer characterization of a BTF under (bio)surfactants addition. In this study, the effect of (bio)surfactant addition on the gas-liquid mass transfer characteristics of two BTFs was measured by using oxygen (O2) as a model gas. Through an empirical correlation, the mass transfer coefficients (kLa) of two hydrophobic VOCs, toluene and hexane, which are of industrial and environmental significance, were estimated. One BTF was filled with expanded perlite, while the other with a mixture of compost and wood chips (C + WC). Both BTFs were operated under different liquid velocities (UL: 0.95 and 1.53 m h−1). Saponin, a biological surfactant, and Tween 80, a synthetic surfactant, were added to the recirculating liquid at different critical micelle concentrations (CMCs: 0–3 CMC). The higher interfacial and surface area of the perlite BTF compared to the C + WC BTF led to higher kLaO2 values regardless of the operational condition: 308 ± 18–612 ± 19 h−1 versus 42 ± 4–177 ± 24 h−1, respectively. Saponin addition at 0.5 and 1 CMC had positive effects on the perlite BTF, with kLaO2 values two times higher compared to those at 0 CMC. Tween 80 exhibited a neutral or slightly positive effect on the mass transfer of both BTFs under all conditions. Overall, the CMC, along with the physical characteristics of the packing materials and the operational conditions evaluated explained the results obtained. This study provides fundamental data essential to improve the performance and design of BTFs for hydrophobic VOCs abatement.

Spatial and temporal variation of PM2.5 and the influence of vegetation in the Yangtze River Delta region

With rapid urbanisation in China, PM2.5 has become a limiting factor for the sustainable development of cities. Taking the Yangtze River Delta as the experimental area, this study analysed the spatial and temporal changes of PM2.5 concentrations from 2001 to 2020. It also examined the variations, dispersion, and correlation with NDVI of PM2.5 concentrations in different vegetation zones at different temporal and spatial scales. The results showed that: (1) The PM2.5 concentration in the Yangtze River Delta showed an overall decreasing trend during 2001–2020, and the change was divided into two phases, starting with an increasing phase and entering a decreasing phase after 2013. (2) In terms of spatial distribution, PM2.5 concentrations in the Yangtze River Delta show a pattern of low in the south and high in the north, with the spatial focus shifting to the north over time. There is a concentration of high levels of particulate matter in the Hefei-Nanjing-Wuxi area. (3) The effect of natural vegetation on the reduction and stabilization of atmospheric particulate matter concentration is better than that of artificial vegetation. (4) Needleleaf forests, broadleaf forests, and shrubs in natural vegetation are more capable of reducing and stabilizing atmospheric particulate matter than grasses. The study can provide a reference for regional air pollution control and regional plant system construction.

Research on the distribution and influencing factors of fine mode aerosol optical depth (AODf) in China

Analyzing the distribution and causes of fine mode aerosol optical depth (AODf) is important for assessing the impact of air pollution on human health and sustainable development. Based on remote sensing data and station information, the study uses Theil Sen's method and the Mann-Kendall test to analyze the spatio-temporal distribution and trends of AODf. Meanwhile, the study used the Extreme Gradient Boosting (XGBoost) model and the Shapley Additive Explanations (SHAP) algorithm to analyze the spatial drivers of AODf and interactive effects. The results show a trend of an initial increase followed by a decline in AODf across China and in most city clusters, with AODf peaking around 2011. Areas with high AODf are mainly concentrated in the eastern and central regions. On a seasonal scale, the average AODf is highest in summer (0.25), followed by winter (0.23), autumn (0.21), and spring (0.19). The AODf, mainly in the eastern and central regions, showed rapid increases and decreases in the 2000–2013 and 2013–2020 periods, respectively. Secondly, the most important factor influencing AODf is sulfate (SO4), contributing 54.84%. This is followed by elevation (DEM) at 10.31%. The SO4, organic carbon (OC), and DEM to AODf are 20–54%, 10–44%, and 11–45% respectively at the city cluster level. Lastly, the response thresholds of AODf to the main factors show significant spatial heterogeneity. The thresholds for SO4 and OC to increase AODf are in the range of 5–16 μg/m3 and 1–10 μg/m3, respectively. The study is of great importance for a comprehensive understanding of the variation patterns of AODf and for the formulation of tailored air pollution control policies based on local conditions.

Role of Law in Combatting Air Pollution

Air pollution in urban areas arises from multiple sources, which may vary with location to location and activity to activity of development. Anthropogenic activities such as rampant industrialization, exploitation and over consumption of natural resources, population explosion are major contributors of air pollution. Regulations and legislations adopted are enumerated. The Clean Air Act envisaged guidelines for industries regarding emission. It has been seen and observed that in spite several rules and regulations, not much has been achieved in controlling air pollution particularly in countries like India. India needs to make tough legislations The present paper is an effort to discuss various aspects of air pollution and control legislation in India emphasizing on the history, present scenario, gaps and drawbacks. It also presents legislative controls with judicial response to certain landmark judgments related to air pollution. The down sides related to enforcement mechanism for the effective implementation of environmental laws for air pollution control have been highlighted.

Forecasting air quality Index in yan’an using temporal encoded Informer

Predictions of the Air Quality Index (AQI) can provide information on air quality, aiding individuals in personal protection and environmental conservation, and facilitating policymakers in implementing measures to control air pollution. In the prediction of AQI time series, single-step forecasting tasks are less challenging. However, as the length of the forecast sequence increases, the prediction error tends to grow remarkably. Furthermore, tacking only univariate features as model inputs can lead to biases in forecasting outputs, as the models fail to capture the overall trends of the series based on a single feature. To address this issue, we developed a novel time series prediction model named Temporal feature Encoded Informer (TE-Informer) using the historical air pollution dataset from Yan’an City. This model incorporates attention mechanisms and periodic encoding, utilizing multiple pollutant series as feature inputs. It enriches the Informer architecture with more comprehensive temporal and global features to predict AQI series. The experimental results demonstrated that the Informer model, enhanced with periodic time encoding, has a significant advantage in extracting features from the input information. In the context of multi-step AQI time series forecasting, the model achieved an MSE (Mean Squared Error) of 24.8692 and an R2-score of 0.9793, outperforming other comparative models in all comprehensive metrics. Consequently, this model can effectively improve prediction accuracy and better capture important characteristic information within the AQI time series. The source code is available at https://github.com/msskx/TE-Informer.

Automated Intelligence: Enhancing Environmental Protection with AI and Electrical Systems

The global community is increasingly focusing on conservation. To combat pollution andresource wastage, modern technologies, like automation and AI are widely employed in environmentalprotection tools. This study delves into the utilization and advancement of automation and AI inenvironmental protection tools examining their real world applications across sectors like wastewatertreatment, air pollution control, solid waste management, metallurgy and energy supervision. Furthermoreit investigates the fusion of automation and artificial intelligence in the development of environmentalprotection tools. The study also addresses aspects such as data collection, performance monitoring,sustainable growth, prevailing challenges and future trends. Through an analysis it underscores thepotential of automation and artificial intelligence in enhancing environmental protection tools effectivenesstowards achieving sustainable conservation goals. Lastly recommendations for research are proposed todrive innovation and enhancement of environmental protection technology. This study aims to offerinsights and guidance for both research endeavors and practical applications, within the realm ofenvironmental protection technology.

The influence of concentration and size on the error of particulate matter detection using charge induction method

Particulate matters generated from waste incineration carry charge due to collision and friction. By using charge induction method, it becomes feasible to detect particulate matter concentration by capturing the electrical signals emitted by particulate matters. In this study, a new charge induction device was constructed and tested. The investigation revealed a linear relationship between the sine wave eigenvalues of the electrical signals and the concentration of particulate matters. The corresponding formulas for peak-to-peak value, root mean square, and standard deviation were calculated, with R2 values greater than 0.98. Additionally, the influence of concentration and size on detection error was studied. The results showed that as the concentration increased or the size decreased, the detection error decreased. Furthermore, the study found that the impact of particulate matter concentration on detection results mitigated that of particulate matter size. The detection device, correlation formulas and influencing factors proposed in this study are expected to provide technical support and theoretical basis for particulate matter detection, offering significant value in the field of air pollution control.

A hybrid technology for enhancing VOC transfer in liquid desiccant dehumidifiers: Combining surfactant and internal cooling

Indoor air pollution control has increasingly focused on addressing the presence of volatile organic compounds (VOCs). In this regard, liquid dehumidification is a potential method for VOC removal. However, most of the recent studies focus on the utilization of conventional liquid desiccants and dehumidifiers without demonstrating their potential in VOC removal. To realize this potential and improve removal efficiency, a proper enhancement strategy is needed. In this paper, a hybrid technology named “surfactant + internal cooling” is proposed to enhance the VOC transfer mechanism. A theoretical model is proposed for predicting VOC transfer employing the hybrid technology presented in this study. The proposed technology is compared against other two conventional technologies in terms of removal performance, parameter distribution, and enhancement mechanism. Additionally, the impacts of some of the key parameters on the VOC removal characteristics when employing the hybrid technology are identified, and their respective distributions are characterized and investigated. The results showed that after the implementation of surfactant technology and internal cooling technology, the improvement rates compared to traditional adiabatic dehumidifiers are 22 % and 42 %, respectively. However, with the hybrid technology combining the surfactant and internal cooling, a maximum improvement rate of 63 % is reported. This enhancement can be attributed to the collaborative reduction of the Henry’s law constant by 62 % at the dual-scale level of micro-solution and macro-structure. Overall, it is demonstrated in this paper that the use of the hybrid technology can simultaneously improve the micro and macro driving forces driving the VOC transfer, enhancing the VOC removal performance. The findings of this study provide new insights to improve the performance of liquid desiccant dehumidifiers in VOC removal applications.

Technological solutions for air pollution control to mitigate climate change: an approach to facilitate global transition toward blue sky and net-zero emission

Technological solutions for air pollution control to mitigate climate change: an approach to facilitate global transition toward blue sky and net-zero emission