Pollutant Concentrations Research Articles

Multiscale CFD analysis of urban air pollution dome and ventilation enhancement via an urban chimney

Air pollution episodes are critical environmental challenges in urban areas, primarily linked to the lack of ventilation during calm and stable atmospheric conditions, rather than a significant increase in emissions. The prevailing circulation pattern under these conditions is the Urban Heat Island Circulation (UHIC), which governs the characteristics of the urban pollution dome and the distribution of pollutants. This study aims to assess the UHIC's influence on spatial and temporal variations in pollutant concentrations and to investigate the efficiency of an Urban Chimney (UC) in improving air quality. Due to the interaction of micro- and meso-scale flows, a multi-scale analysis is required. For this purpose, a pressure-based CFD solver is adapted for analyzing airflow and pollutant dispersion in a multi-scale environment. A coordinate transformation method is used to account for meso-scale effects, and the resulting source terms are applied to the solver. The species transport equation is transformed to the new coordinate system, and the effects of species diffusion on enthalpy transport are included in the governing equations. The results demonstrate that UHIC reduces the average pollutant concentration and causes pollution peaks near ground level and at the city center. The interaction of micro-scale flow within the chimney with meso-scale UHIC impacts the flow field and pollutant concentration across the entire domain, decreasing the urban plume's vertical velocity by 40 percent and the mixing height at the city center by 20 percent. It also increases the vertical velocity within the UC by 23 percent. The UC can serve as a controlling tool for urban ventilation, and its capacity to remove pollutants increases sixfold when interacting with UHIC, reducing pollutant concentration citywide.

Effect of UV exposure and natural aging on the in vitro PAHs bioaccessibility associated with tire wear particles in soil

Tire wear particles (TWP), as an emerging type of microplastics, are a significant source of contaminants in roadside soils due to their high concentration of pollutants, including polycyclic aromatic hydrocarbons (PAHs). This study explored the impact of ultraviolet (UV) exposure and natural aging on the in vitro bioaccessibility of PAHs associated with TWP in soil on a China-wide scale. Our findings suggested that UV exposure amplified the negative charge of TWP by 75 % and increased the hydrophobic groups on the particle surface. The bioaccessibility of 3- and 4-ring PAHs in TWP was significantly (p < 0.05) heightened by UV exposure. After 20 types of soils containing 2 % UV-exposed TWP underwent natural aging, the bioaccessibility of PAHs saw a significant decrease (p < 0.05) to 16–48 %, compared to 28–96 % in the unaged group. Soil pH and electrical conductivity (EC) were the two primary soil properties positively influencing the reduction of in vitro PAHs concentration and PAHs bioaccessibility. According to the prediction results, soils in southern China presented the highest potential region for the release of bioaccessible PAHs from TWP, highlighting the regional specificity of environmental impact. Our study provides valuable insights into the biological impact of PAHs associated with TWP on a regional scale, and offers scientific evidence for targeted soil risk management strategies.

Utilization of photocatalytic degradation and efficiency of engineered geopolymer composite tile doped with nano-particles under ultraviolet light

This study investigates the impact of nano-zinc oxide (ZnO) on the self-cleaning and microstructural performance of engineered geopolymer composites (EGC) for urban cleanliness enhancement. The research focuses on achieving homogeneous dispersion of nano-ZnO through ultrasonication to prevent agglomeration and assess its effects on workability, setting time, mechanical, and microstructural properties of EGC. Results show that incorporating ZnO nanoparticles at 5 % and 10 % concentrations as a surface coating on engineered geopolymer composite creates a highly effective photocatalytic material. This enhanced composite demonstrates significant capability in degrading Rhodamine B (RhB) under UV light exposure over a 15-day period, breaking down organic pollutants into simpler, potentially less toxic substances. The efficiency of degradation depends on factors such as nano-ZnO concentration, UV radiation intensity, and initial pollutant concentration. The optimal dosage of nano-ZnO in EGC is found to be 0.5 %, which enhances hydration and pozzolanic activity, promoting a densified microstructure at the nanoscale. This innovative composite material shows promise as a self-cleaning finishing material for urban buildings, offering potential improvements in both aesthetics and environmental sustainability.

Contribution of congested traffic flow condition to air pollution at intersections in Addis Ababa, Ethiopia

Currently, one of the serious problems in the world is transport-related air pollution. Air pollution from vehicles was not considered properly in the plan, design, and management system of the roads in Addis Ababa city, to solve the problem of traffic congestion. It is influenced by different factors such as road geometry, road surface type, traffic congestion, traffic characteristics, fuel type, and meteorological parameters. This study was aimed to investigate the contribution of congested traffic flow conditions on air pollution at intersection points in Addis Ababa city. To carry out the study, seven intersection points were selected in the city randomly. For the measurement technique of carbon monoxide (CO) and sulfur dioxide (SO2) gases, a portable Aeroqual series 500 instrument was exercised, and for the particular matter (PM2.5 and PM10), Light Amplification by Stimulated Emission of Radiation (LASER) was adapted. To count the number of traffic on the site, a video camera method was utilized. For the data analysis techniques, a multiple linear regression method was practiced, which is very powerful for multiple independent variables with a single dependent one. The study revealed that the level of traffic congestion at each selected intersection is at a severe level. During the congested time, as a base of the steady condition, the pollutants concentration of CO, SO2, PM2.5, and PM10 were increased by an average of 19.10, 51.61, 33.83, and 29.07 % respectively. Based on the analyzed results, when the concentration of pollutants increases, the traffic volume, percentage of heavy vehicles, green time, and approach grade are also increased. On the other hand, the concentration of pollutants decreased when the lane width and wind speed increased. Therefore, any concerned bodies in and around Addis Ababa city should take remedial measures to decrease the emission of the concentration of the pollutants. This investigation is very important for policymakers, municipalities, and planners to do any activities in the city. Researchers can also use it as a reference, for further investigations and remedial measurements.

Mapping air quality trends across 336 cities in India: Insights from three decades of monitoring (1987–2019)

Over a span of 34 years (1987–2019), an in-depth analysis of PM10, SO2, and NO2 trends across India was conducted using data from the National Ambient Air Quality Monitoring Programme’s manual monitoring stations in 336 cities. The study encompassed six geographical regions over three time blocks, revealing a correlation between the expansion of monitoring networks and the nation’s economic growth. Regions like the densely populated Indo-Gangetic Plains (IGP) and Central India consistently hosted more monitoring stations, while the Himalayan and Northeast regions saw substantial increases from initial scarcity. SO2 concentrations showed a declining trend, while NO2 levels remained relatively stable with intermittent fluctuations. Conversely, national average PM10 concentrations exhibited an upward trajectory, notably spiking by 128 % between 2006 and 2009 due to economic activities, construction, network expansion, the 2009 drought, and heightened coal consumption. Spatially, pollutant concentrations across three blocks demonstrated improved SO2 levels, several cities exceeding NO2 standards, and persistently high PM10 levels in the IGP. PM10 levels in block 3 were lower than in block 2, reflecting effective policy interventions. State rankings, however, did not consistently reflect pollutant trends across blocks. Regionally, the IGP consistently had the highest PM10 concentrations, while the Northeast recorded the lowest. Population-weighted exposure levels indicated an overall increase in public exposure to PM10. Analysis of major city per region aligned with national trends, as evidenced by Delhi (IGP), Guwahati (Northeast), Vadodara (Northwest), and Bhopal (Central) showing increased PM10 concentrations since 2006, followed by intermittent declines. In contrast, Shimla (Himalayan) and Chennai (Southern) exhibited distinct patterns. Major industrial cities such as Parwanoo, Bongaigaon, Angul and Talcher, and Visakhapatnam mirrored national trends, with PM10 levels rising since 2009, highlighting the significant impact of industrial activities on air quality. This research underscores the need for targeted, effective mitigation strategies based on spatial and temporal pollutant trends.

Spatiotemporal Changes in Air Pollution within the Studied Road Segment

Environmental protection is a pivotal element of sustainable development, both essential and indispensable in the construction of smart, green cities. Road transport contributes significantly to atmospheric pollution, accounting for as much as 25% of annual emissions within the European Union (EU). To combat the adverse effects of road transport, the EU has set targets to reduce greenhouse gas emissions from both passenger and commercial vehicles. Consequently, sustainable air pollution management has become a focal point for numerous researchers. This study continues the investigation into the distribution of air pollutants along a specific highway segment in Poland. The article addresses two primary research questions: first, the temporal and spatial variations in air pollution adjacent to a major highway in Poland, and second, whether emission levels exhibit significant annual differences and if there is a correlation between pollutant concentrations and the distance from the roadway. The findings offer valuable data on one of the principal substances polluting the air along EU transportation routes. Moreover, the analysis provides recommendations for future road infrastructure renovation projects and strategies to protect the public from harmful traffic-related pollutants, thereby supporting the development of green cities in accordance with sustainable development principles.

Contribution to the study of possible transport of PM10 aerosols in the eastern part of the Czech Republic

The article deals with the assessment of air pollution in the eastern regions of the Czech Republic: Moravia and Silesia, where the limits for the protection of human health for PM and benzo[a]pyrene are significantly exceeded, especially in the north-eastern area. The extent to which this heavily polluted part of Moravia and Silesia affects the central, southern and south-eastern parts of Moravia, i.e. south-eastern part of the Czech Republic, has often been discussed but not proven. The assessment demonstrated the influence and extent of pollutant transport by determining the prevailing daily wind direction. The prevailing NE wind direction results in worse dispersion conditions and higher PM10 concentrations throughout the study area. Conversely, the SW wind direction is a carrier of better dispersion conditions with lower PM10 concentrations in the area. The effect of transport of PM10 pollution in the daily type from the NE direction can be observed at those sites located in the NE of the area of interest from the border with Poland to the Prerov site. In the case of more southerly sites, the methodology used does not allow to determine whether and to what extent they are affected by pollution transport from the northeastern part of Moravia or from neighbouring Poland. This methodology is particularly useful when only 24-h pollutant concentrations are available (not at more detailed, e.g. 1-hour intervals). The internal methodology of the Czech Hydrometeorological Institutefor calculating back trajectories of wind from ground stations is particularly applicable for detecting the origin of short-term episodes with high pollutant concentrations.

River Flashiness in Great Britain: A Spatio-Temporal Analysis

Flashiness refers to the rapidity and frequency of fluctuations in river flow. It can provide insights into flooding, by capturing dramatic increases in river flow, as well as contaminant transport, relating to concentrations of diffuse pollution. Despite a very well gauged river system, there is limited research in Great Britain targeting this component of river flow. This study addresses that gap in knowledge, with a detailed spatio-temporal analysis of river flashiness in Great Britain. Using 513 gauging stations, with historical records of at least 30 years, the average Richards–Baker flashiness index (RBI¯) was calculated for 1990–2020, showing an overall west- (0.6–0.8) to east-coast (0.1–0.2) gradient, being higher in the west (with the exception of some gauges in the south-east). Employing random forest models, the main predictor for flashiness was found to be soil composition, with some additional region-specific predictors. These include flood attenuation by reservoirs and catchment areas, affecting flashiness in the north and west of Great Britain. Additionally, using a subset of 208 gauging stations with data recorded from 1970 to 2020, a temporal analysis examined significant breakpoints and/or trends in yearly flashiness, using the Pettitt test and Mann–Kendall trend test, respectively. Increases in flashiness were found mainly in the north-east and south-west of Great Britain, with implications in flooding and river health. On a seasonal scale, and using a monthly RBI¯, the timing of flashy events was found to oscillate between autumn and spring over the 50 years, gravitating around winter.

Long-term exposures to low concentrations of source-specific air pollution, road-traffic noise, and systemic inflammation and cardiovascular disease biomarkers

ObjectivesAir pollution and traffic noise are detrimental to cardiovascular health. However, the effects of different sources of these exposures on cardiovascular biomarkers remain unclear. We explored the associations of long-term exposure to source-specific air pollution (vehicular exhausts and residential woodsmoke) at low concentrations and road-traffic noise with systemic inflammation and cardiovascular disease biomarkers. Material and methodsModeled outdoor exposure to fine particulate matter (aerodynamic diameter ≤2.5 μm; PM2.5) from vehicular exhausts and residential woodsmoke, nitrogen dioxide (NO2) from road traffic, and road-traffic noise were linked to the home addresses of the participants (Finnish residents aged 25–74) in the FINRISK study 1997–2012. The participants were located in the cities of Helsinki, Vantaa, and the region of Turku, Finland. The outcomes were high-sensitivity C-reactive protein (CRP), a biomarker for systemic inflammation, and cardiovascular disease biomarkers N-terminal pro-B-type natriuretic peptide (NT-proBNP) and troponin I. We performed cross-sectional analyses with linear and additive models and adjusted for potential confounders. ResultsWe found no association between PM2.5 from vehicular exhausts (% CRP difference for 1 μg/m3 increase in PM2.5: −0.9, 95% confidence interval, CI: −7.2, 5.8), or from residential woodsmoke (% difference: −8.1, 95% CI: −21.7, 7.9) and CRP (N = 4147). Road-traffic noise >70 dB tended to be positively associated with CRP (% CRP difference versus noise reference category of ≤45 dB: 18.3, 95% CI: −0.5, 40.6), but the association lacked significance and robustness (N = 7142). Otherwise, we found no association between road-traffic noise and CRP, nor between NO2 from road traffic and NT-proBNP (N = 1907) or troponin I (N = 1951). ConclusionLong-term exposures to source-specific, fairly low-level air pollution from vehicular exhausts and residential woodsmoke, or road-traffic noise were not associated with systemic inflammation and cardiovascular disease biomarkers in this urban area.

Precise management and control around the landfill integrating artificial intelligence and groundwater pollution risks

The Landfill plays an important role in urban development and waste disposal. However, landfill leachate may also bring more serious pollution and health risks to the surrounding groundwater environment. Compared with other areas, the area around the landfill needs more precise management. To solve this problem, based on the “pressure-state-response” framework, a method for the identification and evaluation of groundwater pollution around the landfill was constructed. The LPI method was used to assess the contamination potential of the leachate. The comprehensive quality of groundwater was evaluated by the entropy-AHP water quality assessment method, sodium adsorption ratio and sodium percentage. The probabilistic health risks of groundwater were assessed based on a Monte Carlo algorithm. The sources of pollutants were identified by comprehensively using the PCA-APCS-MLR model and the PMF model. Finally, the self-organizing map algorithm and the Kmeans algorithm were integrated to enhance the precision of groundwater management and control measures. The results showed that the leachate of the landfill was in the mature stage, and the concentration of inorganic substances was relatively high. Leachate had the potential to contaminate surrounding groundwater. The groundwater quality of 68.14% of the study area was in the poor or lower level. The groundwater near the landfill was unsuitable not only for drinking but also for irrigation purposes. Cl− was the main non-carcinogenic risk factor. Reducing pollutant concentration and controlling exposure time are effective strategies for mitigating health risks caused by high-concentration pollutants (Cl−, NO3−) and low-concentration pollutants (F−), respectively. The groundwater around the landfill was jointly affected by six pollution sources. The PMF model has better analytical ability in mixed pollution areas. The groundwater in the study area was divided into five clusters, of which cluster Ⅰ was significantly affected by leachate, and cluster Ⅴ had the lowest pollution and health risk.

New insight to the enriched microorganisms driven by pollutant concentrations and types for industrial and domestic wastewater via distinguishing the municipal wastewater treatment plants

Enriched microbial communities and their metabolic function were investigated from the three wastewater treatment plants (WWTPs), which were CWWTP (coking wastewater), MWWTP1 (domestic wastewater), and MWWTP2 (mixed wastewater with domestic wastewater and effluent from various industrial WWTPs that contained the mentioned CWWTP). Pollutant types and concentrations differed among the three WWTPs and the reaction units in each WWTP. CWWTP had a higher TCN and phenol concentrations than the MWWTPs, however, in MWWTP2 no phenol was discovered but 0.72 mg/L TCN was found in its anaerobic unit. RDA results revealed that COD, TN, TP, TCN, NO3−-N, and phenol were the main factors influencing the microbial communities (P < 0.05). CPCoA confirmed the microbial community difference driven by pollutant types and concentrations (65.1% of variance, P = 0.006). They provided diverse growth environments and ecological niches for microorganisms, shaping unique bacterial community in each WWTP, as: Thiobacillus, Tepidiphilus, Soehngenia, Diaphorobacter in CWWTP; Saccharibacteria, Acidovorax, Flavobacterium, Gp4 in MWWTP1; and Mesorhizobium, Terrimicrobium, Shinella, Oscillochloris in MWWTP2. Group comparative was analyzed and indicated that these unique bacteria exhibited statistically significant difference (P < 0.01) among the WWTPs, and they were the biomarkers in each WWTP respectively. Co-occurrence and coexclusion patterns of bacteria revealed that the most of dominant bacteria in each WWTP were assigned to different modules respectively, and these microorganisms had a closer positive relationship in each module. Consistent with the functional profile prediction, xenobiotics biodegradation and metabolism were higher in CWWTP (3.86%) than other WWTPs. The distinct functional bacteria metabolized particular xenobiotics via oxidoreductases, isomerases, lyases, transferases, decarboxylase, hydroxylase, and hydrolase in each unit or WWTP. These results provided the evidences to support the idea that the pollutant types and concentration put selection stress on microorganisms in the activated sludge, shaping the distinct microbial community structure and function.

Air pollutant removal performance using a BiLSTM-based demand-controlled ventilation method after tunnel blasting

Efficient tunnel ventilation is essential for ensuring construction safety and protecting personnel health during tunnel construction. This study proposes a demand-controlled ventilation (DCV) method on the basis of deep learning algorithm to both improve pollutant removal efficiency and reduce energy consumption. The DCV method utilizes a two-layer bidirectional long short-term memory algorithm (BiLSTM) to predict pollutant concentrations. The air volume is dynamically adjusted based on the gaseous pollutant removal requirements. The coefficient of ventilation performance (COVP) is proposed to evaluate the performance of two ventilation methods (DCV and constant air-volume ventilation (CAV)) through computational fluid dynamics (CFD) simulations. The results show that the DCV results in a lower maximum average CO concentration and higher removal efficiency in the heading area (372.3 mg/m3) than the CAV does (404.1 mg/m3). The fan's energy consumption of DCV is 64.6% lower than that of CAV during a 1000 s ventilation period. The COVPs for both methods exhibit temporal variation and achieves their maximums (2.25 for DCV and 0.741 for CAV) after reaching the constraint conditions (air volume threshold). The DCV method expedites pollutant elimination, reduces construction waiting period, and minimizes energy consumption, providing a novel application of a deep learning algorithm in construction engineering.

Effects of NOx and NH3 on the secondary organic aerosol formation from α-pinene photooxidation

Understanding the effects of mixed anthropogenic pollutants on the photooxidation of volatile organic compounds (VOCs) is essential for unraveling the formation pathways of secondary organic aerosols (SOA). Yet, it remains a highly challenging experimental target owing to the complexities in the precise measurement of molecular compositions of products and number/mass concentrations of particles as a function of pollutant concentration in the ambient atmosphere. Here, a series of well-defined chamber experiments were performed to explore the effects of NOx and NH3 on the SOA formation from photooxidation of the most abundant monoterpene, α-pinene. The results indicate that the suppression effect of NO and NO2 on the α-pinene photooxidation shows monotonous and parabolic trends, respectively. The presence of NH3 enhances particle number concentrations during the α-pinene + NOx photooxidation by participating in reactions with organic acids. New compounds, including organic peroxides, esters, organic nitrates, and peroxyacyl nitrates, are observed at molecular weight (MW) = 166, 173, 217, 231, 280, 282, 304, and 410 through threshold photoionization making use of a recently constructed vacuum ultraviolet free electron laser in positive ion mode. The molecular structures and formation paths of these species are speculated, which advance the category of VOC oxidation products. Our study provides significant understanding of the influence of NOx and NH3 on the VOC photooxidation, which can be utilized to establish predictive SOA formation networks and to improve atmospheric models.

Short-term air pollution prediction using graph convolutional neural networks

Pollution is a major concern in the present day, causing multiple illnesses and deaths, specifically in developing countries in Asia and Africa. While it has drawn worldwide attention as governments try to issue laws to meet certain criteria for air pollution levels, pollution concentration forecasting has become a major challenge. Particularly, short term forecasting will help to gain information regarding concentrations of harmful pollutants for the upcoming hours and enable better decision-making with regards to controlling air pollution. In this paper, we investigate spatio-temporal graph-based models to determine the best methods for spatial and temporal analysis of data. The models have the additional capacity to perform multi-variate predictions of correlated data, i.e., predicting multiple pollutant concentrations simultaneously, thus requiring lower computational efforts. A real-world pollution dataset measured over Delhi, India, is used to comparing the proposed models with baselines, which shows the Spatio-Temporal Graph Convolution Neural Network (STGCN) models to be performing better than others. For a better understanding of model architectures with the most effective strategies for spatial and temporal data analysis, three models, namely STGCN-A, STGCN-B, STGCN-C have been developed. The models have been compared with 6 other baselines over multiple forecasting horizons of 1 h, 24 h, and 48 h timesteps using various metrics such as mean absolute error (MAE), root mean square error (RMSE), mean absolute percent error (MAPE). On the PM2.5 dataset of Delhi, STGCN-B achieves a performance of 10.53 MAE, 6.92 RMSE and 25.25 MAPE for a 1 h forecast, while STGCN-C achieves 20.18 MAE, 14.73 RMSE and 55.45 MAPE for a 24 h forecast. In general, both structures achieve similar results, with STGCN-C being better in many cases. They are further analysed through observation-prediction graphs and Taylor diagrams, which give an insight into our findings. The models are additionally validated on a benchmark real-world dataset from California, USA for better understanding of the spatio-temporal relations and model performances on a more stable dataset, where STGCN-C performs best for PM2.5 with 4.30 RMSE, 1.98 MAE, 25.96 MAPE for 1 h predictions for univariate data and 3.63 RMSE, 1.88 MAE and 25.91 MAPE in multivariate forecasting. The developed spatio-temporal graph-based models hold promising applications in urban air quality management, aiding policymakers in implementing targeted interventions to mitigate pollution-related health risks. Furthermore, these models can support public health agencies by providing timely and accurate forecasts of pollutant concentrations, enabling proactive measures to safeguard community well-being. Our study showcases the efficacy of spatio-temporal graph-based models in accurately forecasting air pollutant concentrations, with particular emphasis on short-term predictions. By leveraging multi-variate capabilities, our proposed models demonstrate superior performance compared to baseline approaches across various forecasting horizons.

Macroscopic and molecular scale assessment of thermal effects on soil-water interactions of unsaturated diesel-contaminated soils

The soil-water interactions of unsaturated diesel-contaminated soil are crucial for assessing pollution transport during thermal remediation. This paper aims to improve our understanding of this issue by measuring the matric suction of unsaturated contaminated kaolin and carrying out molecular dynamics simulations under thermal conditions. Results show that the increase in pollutant concentration could reduce the water retention capacity of diesel-contaminated kaolin due to changes in electrochemical properties and pore characteristics of samples, as well as a decrease in interfacial tension. On the other hand, pollutants formed a protective film on the kaolinite surface to act as a liquid bridge and prevent water loss at higher temperatures, as confirmed by Fourier transform infrared spectroscopy. With rising temperatures (50–60 °C), kaolin matric suction generally decreased with higher pollutant concentrations, but this trend was not very evident at lower pollution concentrations (0–10,000 mg/kg). In addition, molecular dynamics simulations were used to demonstrate the validity of these findings. The presence of pollutants might strengthen the interaction energy between kaolinite and water (for example, increasing from 276.52 kcal/mol (25 °C) and 267.95 kcal/mol (40 °C) at 8000 mg/kg to 296.54 kcal/mol (25 °C) and 292.46 kcal/mol (40 °C) at 10,000 mg/kg), thereby enhancing the water retention capacity of kaolin. In short, the study revealed that the coating of pollutants on kaolinite could act as a protective film, which binds water molecules through van der Waals and electric field forces and thereby reduces the sensitivity of water retention capacity to temperature.

Data-driven multi-objective intelligent optimal control of municipal solid waste incineration process

The municipal solid waste incineration (MSWI) process has become the primary technology for municipal solid waste (MSW) treatment worldwide due to its advantages of harmlessness, reduction, and resource recovery. However, the automatic combustion control (ACC) system developed in developed countries are often not effectively applicable to other countries, particularly China, due to regional differences in MSW composition. To achieve intelligent development of MSWI plants, it is crucial to develop intelligent optimal control technology tailored to national conditions to reduce comprehensive pollutant emission concentration (CPEC) and improve combustion efficiency (CE). This article proposes a data-driven multi-objective intelligent optimal control strategy. Firstly, based on the analysis of the whole process, the Tikhonov regularization-least regression decision tree (TR-LRDT) algorithm is used to establish a MSWI whole process model, incorporating serial controlled objects and parallel pollutant indicators, to support a multi-objective optimization model. Then, leveraging the ACC system and expert experience, a multi-input multi-output loop controller is established using the single neuron adaptive PID (SNA-PID) algorithm to achieve stable control of key controlled variables. Next, a mutation crossover strategy and termination condition are integrated with the multi-objective particle swarm optimization (MOPSO) algorithm to solve the multi-objective optimization model, and domain expert rules are applied to determine the optimal setpoints for key controlled variables in the Pareto front, aiming to reduce CPEC and improve CE. Finally, the effectiveness of the proposed intelligent optimal control strategy is validated using actual data. The experimental results demonstrate that this strategy not only maintains the stability of key controlled variables but also decreases CPEC by 1.82% and increases CE by 2.38%, laying a foundation for further research into intelligent optimization control of the MSWI process. Further, the software system based on the proposed strategy is developed and validated on a hardware-in-loop simulation platform.

Ozone/biological aerated filter integrated process for recycled paper mill wastewater: A pilot-scale study

In this study, the effluent from recycled paper mill was treated using a combined ozone (O3) and biological aerated filter (BAF) process. Key operational parameters such as ozone dosage, pH, hydraulic retention time (HRT), volume load and gas-to-water ratio were optimized. Under optimal conditions, with a total ozone dosage of 100 g/m, a gas-to-water ratio of 4:1, and an HRT of 3.0 hours in the BAF, the chemical oxygen demand (COD) and chroma of the treated wastewater were reduced to 44–55 mg/L and 2–4 PCU, achieving removal efficiencies of 70 % and 95 %, respectively. The discharge effluent not only satisfy the new discharge standard of China (GB3544–2008), but also can be used as recycling water. Additionally, the treatment cost of wastewater was ca. 1.3 ¥/m3 in pilot-scale test, significantly decreasing the cost. Ozone pretreatment has a significant effect on wastewater decolorization by disrupting the molecular chemical structure of pollutants, which increase the biochemical properties of biofilm and is beneficial to the sequential BAF treatment. The sludge in the O3/BAF system exhibited increased biomass with minimal filamentous bacteria and higher dehydrogenase activity, confirming stable and robust bacterial growth. GC-MS analysis revealed substantial reduction in pollutant content and diversity post-treatment, although the recalcitrant compound (Z)-13-docosenamide remained relatively high, decreasing from 27.37 % to 21.14 %. The mechanism of the O3/BAF process for the pollutant degradation were also proposed. This study demonstrated that a combination of ozone and fixed biofilm treatment is an efficient and cost-effective treatment, providing the theory and practical applicability for the industrial wastewater.

Assessing the impact of heavy metals on bacterial diversity in coastal regions of Southeastern India.

Globally, there is growing concern over the environmental contamination of coastal ecosystems caused by anthropogenic activities. Here,we performed a study to evaluate the degree of heavy metal contamination in 5 different sediment samples collected from five sites along the Southeastern coast of India. Additionally, the research aims to explore the potential ecological implications of heavy metal contamination on the bacterial diversity, a crucial factor in upholding a sustainable ecosystem. A total of seven heavy metals, i.e., chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), mercury (Hg), cadmium (Cd) and arsenic (As), were assessed and quantifiedusing inductively coupled plasma mass spectrometry. Targeted amplicon sequencing revealed that phylum Proteobacteria (36.9%) was the most dominating followed by Halobacterota (25.5%), Actinobacteriota (15%), Firmicutes (6.7%), Bacteroidota (4.0%), Thermoplasmatota (2.3%), Acidobacteriota (2.0%), Chloroflexi (1.6%), Planctomycetota (1.2%) and Crenarchaeota (1.1%). According to the alpha diversity estimate, lesser bacterial diversity was observed in areas with high pollution levels. Moreover, the physicochemical parameters of the sediments were analyzed. The contamination levels of the sediments were evaluated using the geo-accumulation index (Igeo), contamination factor (CF) and pollution loading index (PLI) to ascertain the comprehensive toxicity status of the sediments. TheIgeo values revealed sediment pollution with metals such as Hg and Cd. The sediments obtained from the sampling site PU-01 showed the highest concentration of Hg pollution. Considering the ecotoxicological aspect, theestimated risk index (RI) values indicated a range from low to significant ecological risk.

Air pollution and children’s mental health in rural areas: compositional spatio-temporal model

Air pollution stands as an environmental risk to child mental health, with proven relationships hitherto observed only in urban areas. Understanding the impact of pollution in rural settings is equally crucial. The novelty of this article lies in the study of the relationship between air pollution and behavioural and developmental disorders, attention deficit hyperactivity disorder (ADHD), anxiety, and eating disorders in children below 15 living in a rural area. The methodology combines spatio-temporal models, Bayesian inference and Compositional Data (CoDa), that make it possible to study areas with few pollution monitoring stations. Exposure to nitrogen dioxide (NO2), ozone (O3), and sulphur dioxide (SO2) is related to behavioural and development disorders, anxiety is related to particulate matter (PM10), O3 and SO2, and overall pollution is associated to ADHD and eating disorders. To sum up, like their urban counterparts, rural children are also subject to mental health risks related to air pollution, and the combination of spatio-temporal models, Bayesian inference and CoDa make it possible to relate mental health problems to pollutant concentrations in rural settings with few monitoring stations. Certain limitations persist related to misclassification of exposure to air pollutants and to the covariables available in the data sources used.

Sensitivity analysis for sets: Application to pollutant concentration maps

Sensitivity analysis for sets: Application to pollutant concentration maps