Main Pollutants Research Articles

Air Quality Prediction in Smart Environment using Lightweight Residual Network: Sustainable Light-AirNet Approach

In densely populated places, air pollution prediction is crucial since it directly affects human health and the local governance. The main objective of this work is to analyze the spatial and temporal patterns of the concentration of the main air pollutants in Bangalore, India. In this paper, a lightweight residual network with an attention mechanism is created using a collection of residual concatenation blocks layered with recursive residual blocks. This aids in the adaptive extraction of useful features, the learning of more expressive spatial context information, and the efficient transfer of information through gradient flow in the network. A unique attention mechanism, known as the Two-Fold Attention Module, has been created with the purpose of enhancing the model’s ability to represent information. The Light-AirNet model was designed to provide hourly forecasts by using past pollution data and three measured weather variables were collected from weather stations. Light-AirNet is compared with existing approaches in terms of different metrics and it was found that it achieves 24.5% of root-mean-square error, 21.5% of mean square error, 12.59% of mean absolute error, and 97.45% of prediction accuracy.

Analysis of Non-Fuel Evaporative Pollutants Emitted by Automobile Seats

With the increasing stringency of emission standards, the challenge for conventional passenger vehicles to meet evaporative emission requirements has intensified. Evaporative emissions from vehicles are primarily derived from the fuel system and the non-fuel system, with the latter accounting for 30% to 50% of total vehicle emissions. Thus, the study of non-fuel system evaporative pollutants in passenger vehicles has become especially important. This research conducted tests on automobile seat assemblies and their components to identify the main pollutants. Through the contribution of concentration ratios, a predictive formula was established to validate tests related to seat assemblies and components, aiding enterprises in effectively controlling non-fuel evaporative pollutants from automobile seats.

Numerical simulation model of water quality evolution in urban rivers considering adsorption–desorption process of nitrogen and phosphorus in sediments

In order to more realistically simulate the river channel water quality considering the characteristics of sediment release, it is necessary to couple the simulation model of sediment release to the hydrology-hydrodynamics-water quality model to quantify the complexities involved in the nitrogen and phosphorus cycle in river channel sediments. However, in the existing studies, most of the water–sediment models constructed in the analysis of single pollutants such as NH3-N or BOD, but fail to consider the interaction between multiple pollutants, and the model software related to water quality simulation fails to establish complex river sediment pollution simulations, there are still challenges in the application of the existing water quality module of the MIKE simulation software to the nitrogen and phosphorus cycle of sediments. In this study, based on the new sediment nitrogen and phosphorus release mechanism, we rewrite the water quality module ECOLab of the MIKE 21 model. The laboratory test results and the measured data are applied to the calibration of the model parameters. The new water quality module considering the adsorption–desorption process of sediments was embedded into the MIKE 21 model. The new module couples the MIKE model and the SWWM model to establish a 2D water quality model of river channel hydrology-hydrodynamics-water quality-sediment release coupling. This model targeted applies in urban river channels with sediment releasion as the main pollution. The results show that the new model has significantly improved the simulation accuracy of BOD and NH3-N, and has an optimized effect on the simulation of TP. The R, NSE, and RMSE values of the model for NH3-N simulation are 0.904, 0.845, and 0.612. This model is more suitable for numerical simulation of urban river water quality where sediment release is the main cause of endogenous pollution.

Identifying the Local Influencing Factors of Arsenic Concentration in Suburban Soil: A Multiscale Geographically Weighted Regression Approach.

Exploring the local influencing factors and sources of soil arsenic (As) is crucial for reducing As pollution, protecting soil ecology, and ensuring human health. Based on geographically weighted regression (GWR), multiscale GWR (MGWR) considers the different influence ranges of explanatory variables and thus adopts an adaptative bandwidth. It is an effective model in many fields but has not been used in exploring local influencing factors and sources of As. Therefore, using 200 samples collected from the northeastern black soil zone of China, this study examined the effectiveness of MGWR, revealed the spatial non-stationary relationship between As and environmental variables, and determined the local impact factors and pollution sources of As. The results showed that 49% of the samples had arsenic content exceeding the background value, and these samples were mainly distributed in the central and southern parts of the region. MGWR outperformed GWR with the adaptative bandwidth, with a lower Moran's I of residuals and a higher R2 (0.559). The MGWR model revealed the spatially heterogeneous relationship between As and explanatory variables. Specifically, the road density and total nitrogen, clay, and silt contents were the primary or secondary influencing factors at most points. The distance from an industrial enterprise was the secondary influencing factor at only a few points. The main pollution sources of As were thus inferred as traffic and fertilizer, and industrial emissions were also included in the southern region. These findings highlight the importance of considering adaptative bandwidths for independent variables and demonstrate the effectiveness of MGWR in exploring local sources of soil pollutants.

Long-term remote sensing-based methods for monitoring air pollution and cloud cover in the Balkan countries.

The use of remote sensing and GIS methodology has accelerated the processing of data on pollution, but has also raised a question about the accuracy of the same. The research focuses on four main air pollutants (CO, NO, SO2, O3), the data on which were obtained from satellite images of Landsat 8 and Landsat 9, for the period 2000-2020. The data on relative cloudiness were obtained from the database CHELSA (Climatologies at high resolution for the earth's land surface areas) for the period 1980-2010. All the data were further processed and analyzed through the procedures of numerical GIS analysis, multi-criteria analysis, supervised and unsupervised satellite classification, and pixel analysis. The results of the analysis of cloud cover in the Balkan region showed that the month with the highest cloud cover in this period was February, with the maximum of (93.18%), whereas the lowest cloud cover was in July (0.19%). The analyzed period (2000-2010) was in the middle range for the pollutants NO and SO2 and in the lower range for CO; O3. In the period 2010-2020, there were high concentrations of NO, SO2, and CO and low concentrations of O3. The most polluted cities in the last twenty years are Ordu (Turkey), Sarajevo (Bosnia and Herzegovina), and Bor (Serbia). Finally, two most extreme air pollutants in the territory of Balkan countries were SO2 and NO (2000-2020).

Investigation of the effects of offshore wind turbines on marine ecology from a chemical perspective

The offshore wind energy sector is a rapidly growing renewable energy industry worldwide. The concern that this rapid growth may have effects on the marine environment has drawn the attention of scientists in various fields. In this context, this article provides an overview of chemical emissions from sources such as corrosion protection systems and microplastics, which are pollutants for marine ecology. It discusses their significance and potential impacts on the marine environment, as well as proposing strategies to reduce these emissions.
 Corrosion is a common issue for offshore structures, requiring corrosion protection systems to maintain structural integrity. These systems often have galvanic anodes that can release significant amounts of metals directly into seawater. Additionally, microplastics and their compound pollutants are toxic to some marine organisms, affecting their growth, feeding, reproduction, and other physiological activities. Moreover, microplastics can pose potential risks to humans through the consumption of marine and terrestrial food products and drinking water.
 While current assumptions suggest a low environmental impact for these two main pollutants, there is insufficient monitoring data to assess the environmental impact of this new source from a chemical perspective. This study represents the first chemical evaluation of the effects of pollutants such as corrosion protection systems and microplastics used in offshore wind turbines on marine ecology. The results obtained can serve as a reference for many future studies.

Integrated Absorption–Adsorption Process for Waste-Free Decontamination of Gases from Sulfur Dioxide, Part 2: CFD Modeling and Experimental Investigation of a Bubble-Cap Tray

There are many technologies for removal of sulfur dioxide (SO2) from flue gases. They are intrinsic part of the efforts for sustainability of energy production as they reduce the harmful impact of fossil fuel combustion on the environment by minimizing one of the main air pollutants. A wide range of methods use alkaline absorbents. In most cases, the products obtained from the absorption process have to undergo further oxidation, which increases the cost of carrying out the process. As a final result, the sulfates obtained (Na2SO4 and CaSO4) have limited practical application and there is a problem with their disposal. Scientific and engineering efforts have been directed towards the development of a practically waste-free technology for gas purification from SO2. An absorption–adsorption method is proposed, which comprises absorption of SO2 in water with simultaneous adsorption of the resultant sulfurous acid (H2SO3) from the aqueous solution with a synthetic anion-exchange resin. Regeneration of the adsorbent is accomplished with a dilute solution of ammonia (NH3), followed by decomposition of the resulting ammonium sulfite ((NH4)2SO3) with nitric acid (HNO3). The products of the processes are pure gaseous (liquefied) SO2 and an aqueous solution of ammonium nitrate (NH4NO3). Sulfur dioxide has a wide range of applications in the chemical industry; ammonium nitrate is a product with a variety of commercial uses as well, the most common of which is as a soil fertilizer. The new absorption–adsorption method offers a practically waste-free technology. The basic unit of this technology is a bubble-cap tray column where the absorption–adsorption process is carried out in an aqueous suspension of a synthetic anion-exchange resin. This work presents a CFD simulation of the flow on the bubble-cap tray. A physical model of the column is constructed, which contains a bubble-cap tray fabricated by 3D printing. As a result of this experimental study, new data on the tray pressure drop, gas holdup, and the kinetics of the absorption–adsorption process were obtained.

Geochemical baseline establishment, pollution level and health risk assessment of soil heavy metals in the upper Xiaowen River Basin, Shandong Province, China.

This study analyzed the distribution and content of eight heavy metals (Cu, Pb, Zn, Ni, Cr, As, Cd, and Hg) in 221 surface soil samples from the upper reaches of the Xiaowen River. Environmental geochemical baselines were established for the eight heavy metals, and the pollution status was assessed on the basis of these baselines and the soil background value of Weifang City. The calculation results of Nemerow pollution index and the potential ecological hazard index (PEHI)-Ri showed that the overall pollution degree and ecological hazard in the study area were at a slight level. 49% (calculated by baseline value) and 24% (calculated by background value of Weifang City) samples were at moderate or above pollution level. 9% (calculated by baseline value) and 42% (calculated by background value) samples were at the level of moderate potential ecological hazards or above. According to the calculation results of Igeo and PEHI-Ei, the main pollutant in the study area was Hg, followed by Cd. 3% (calculated by baseline value) and 12% (calculated by background value) of Hg samples were at moderate or above contamination levels. 5% (calculated by baseline value) and 38% (calculated by background value) of Hg samples were at the level of strong potential ecological hazard or above. The western, central, and eastern parts of the study area were mainly the primary areas of pollution and ecological hazards. The non-carcinogenic risk was at an acceptable level, the carcinogenic risk was at a tolerable level, and the main risk pathway was oral intake, with Cr being the main contributor. Source apportionment indicated that soil heavy metals primarily originate from soil parent material, transportation, agricultural fertilization, and industrial emissions (waste gas, waste water and solid waste).

Different “nongrain” activities affect the accumulation of heavy metals and their source-oriented health risks on cultivated lands

“Nongrain” production on cultivated land is one of the primary environmental issues in China. Different “nongrain” activities may introduce different pollution sources to the local environment, leading to variations in heavy metal contents in soil, which can profoundly impact national food security. In this study, three typical “nongrain” regions (Nanxun (NX), Xiaoshan (XS) and Lin'an (LA)) with intensive aquaculture, tea planting and flower (seedling) growth on cultivated land around the Hangzhou metropolitan area were selected to address the spatial heterogeneity of accumulation levels, sources and source-oriented health risks of heavy metals in soil. The results showed that Hg was the main pollutant in NX and XS, while Cd and As were the major contaminants in LA. Aquiculture and sericultural industries (37.43%), natural sources (23.59%) and industrial activities (38.99%) were the major sources in NX; atmospheric deposition (37.73%), flower and seedling planting (23.49%) and metal-related industries (35.16%) were the major sources in XS; and atmospheric deposition (28.06%), excessive application of fertilizers and pesticides during tea planting (43.47%) and natural sources (28.47%) were the major sources in LA. The major risk population, area, exposure route and hazardous elements were children, LA, ingestion and As and Cr, respectively. From the perspective of source-based health risk assessment, in addition to natural sources that are difficult to intervene in, industrial activities, especially leather and wood process industries, metal-related industries and excessive fertilizer and pesticide application during tea planting contributed the most to the total health risk, which explained 67%, 41% and 42%, respectively, of the total risk in NX, XS and LA. High health risks are present in sources with heavy loadings of hazardous heavy metals (As and Cr); thus, to protect human health, the corresponding high-risk anthropogenic pollution sources in different “nongrain” areas need to be controlled.

Health risk assessment of ultrafine particle movement in children's residential spaces based on probabilistic neural networks

The air quality in children's living spaces has a significant impact on their health, and ultrafine particulate matter (PM2.5), as one of the main pollutants in the air, poses a particularly prominent threat to children's respiratory and immune systems. Therefore, it important to conduct health risk assessment on ultrafine particle exercise in children's living spaces. In this article, an intelligent risk assessment model of ultrafine atmospheric particles in children's sports space in residential areas based on probabilistic neural network (PNN) is studied. This model utilizes probabilistic neural networks to monitor and predict ultrafine particles in residential spaces, evaluating their potential risks to children's health. By collecting relevant data such as particulate matter concentration and air quality index in residential spaces, and using probabilistic neural networks for training and prediction, accurate assessment of the health risks of ultrafine particulate matter during exercise can be achieved. This model can be applied to densely populated places such as families and schools, providing parents and relevant institutions with scientific risk warning and prevention measures to ensure the health and safety of children. Probabilistic neural networks can learn and simulate complex nonlinear relationships, making the model more accurate in predicting the concentration of ultrafine particles and health risks. So as to construct a method system of health risk assessment of atmospheric particles, and the model is heavy metal elements in atmospheric fine particles, and corresponding improvement measures are put forward according to the assessment results. For circulatory system diseases, the degree of harm of atmospheric temperature and fine particles to children's health is basically close, and with the continuous decline of temperature, the synergistic effect of the two is more obvious. The concentration of atmospheric ultrafine particles significantly affects the health risk of children's sports activities space. All the experiments in this article are quite good. Among them, the average accuracy of the PNN health risk identification model is 0.840, and the average recall rate is 0.837. The health risk assessment model in this article strengthens the correlation of data information through scientific methods, grasps the basic information of children's sports activity spaces in residential areas, and formulates targeted sports health risk assessments for children's sports activity spaces in residential areas. Compared with the comparison method, the response speed is significantly improved, with an accuracy rate of over 90%.

Chronic exposure to PM10 induces anxiety-like behavior via exacerbating hippocampal oxidative stress

As one of the most environmental concerns, inhaled particulate matter (PM10) causes numerous health problems. However, the associations between anxiety behavior and toxicity caused by PM10 have rarely been reported so far. To investigate the changes of behavior after PM10 exposure and to identify the potential mechanisms of toxicity, PM10 samples (with doses of 15 mg/kg and 30 mg/kg) were intratracheally instilled into rats to simulate inhalation of polluted air by the lungs. After instillation for eight weeks, anxiety-like behavior was evaluated, levels of oxidative stress and morphological changes of hippocampus were measured. The behavioral results indicated that PM10 exposure induced obvious anxiety-like behavior in the open field and elevated plus maze tests. Both PM10 concentrations tested could increase whole blood viscosity and trigger hippocampal neuronal damage and oxidative stress by increasing superoxide dismutase (SOD) activities and malondialdehyde levels, and decreasing the expressions of antioxidant-related proteins (e.g., nuclear factor erythroid 2-related factor 2 (Nrf2), SOD1 and heme oxygenase 1). Furthermore, through collecting and analyzing questionnaires, the data showed that the participants experienced obvious anxiety-related emotions and negative somatic responses under heavily polluted environments, especially PM10 being the main pollutant. These results show that PM10 exposure induces anxiety-like behavior, which may be related to suppressing the Nrf2/Keap1-SOD1 pathway.

Comprehensive environmental management research for indoor sports and fitness facilities in community sports center

Formaldehyde, as one of the main indoor air pollutants, poses a significant threat to human health. Therefore, to effectively reduce indoor formaldehyde pollution, we prepare a novel silver carbonate (Ag2CO3)/amorphous silver silicates (a-AgSiO)/g-C3N4 nanosheets (CNNS) composite catalyst using in-situ precipitation and ion exchange methods. The Ag2CO3/a-AgSiO/CNNS composite was characterized by various methods, including XRD, FT-IR, and XPS. Compared to the a-AgSiO/CNNS composite, the formaldehyde degradation efficiency of the Ag2CO3/a-AgSiO/CNNS composite is significantly enhanced, reaching up to 81.9% under the same conditions. Cycling stability tests also demonstrated the excellent chemical stability of the Ag2CO3/a-AgSiO/CNNS composite, with a formaldehyde degradation efficiency of 72.6% after five cycles. By investigating the mechanism of Ag2CO3/a-AgSiO/CNNS composite, we found that the formation of double Z-type heterogeneous structure in the Ag2CO3/a-AgSiO/CNNS composite enhanced the separation efficiency of photoinduced charge carriers and improved the photocatalytic activity. In conclusion, the prepared Ag2CO3/a-AgSiO/CNNS composite catalyst with excellent photocatalytic performance can be used for the efficient degradation of indoor formaldehyde.

Interaction between soil water saturation and toxic element accumulation in woody plants (Freiberg region, Germany)

ABSTRACT Relationships between soil water saturation (SWS) and the accumulation of arsenic (As), cadmium (Cd), and lead (Pb) in soils and aboveground biomass of Populus tremula and Salix caprea were explored. SWS was computed with the hydrological model ParFlow/CLM and compared to data from field observations. Plant and soil samples collected at the main pollution sources contained the highest concentrations of As, Cd, and Pb. S. caprea contained higher concentrations of As, Cd, and Pb than P. tremula. In both species, metal(loid) concentrations in leaves were significantly higher than in branches. At strongly mining-affected locations, metalloid concentration in the trees largely reflected the pollution levels in the soil. In remote study locations, the soil–plant transfer of Cd and Pb was affected by water saturation: element accumulation increased significantly with soil moisture. Results demonstrate that coupling field observations with hydrological modelling is a promising approach for estimating element accumulation.

MONITORING AND ANALYSING AIR POLLUTION IN RESIDENTIAL AREAS UNDER THE INFLUENCE OF VEHICLE AND METALLURGICAL PLANT EMISSIONS

Assessment of the level of air pollution in the residential areas of an urbanized city allows to determine the main pollutants contained in the air and allows to adjust or improve the cleaning equipment of emission sources. The purpose of the work: research of the general level of air pollution in the zone of influence of the central road with heavy traffic and an industrial facility on the air quality of residential areas of the city of Kamianske. Methodology. The work used air quality analyzers Benetech GM 8804, Wintact wt 8811, Benetech GM 8806 to determine the presence (concentration) of hydrogen sulfide, carbon monoxide, ammonia, formaldehyde, dust (PM 2.5), combustible gases (LEL). Sampling points were chosen in residential areas: indoors, on the adjacent territory and near the road. Scientific novelty. In the course of research, it was found that the residential area, which is located closer to the industrial facility and to the central part of the city with shopping centers, has relatively higher LEL indicators and lower indicators of oxygen content in the air. The dynamics of changes in air pollution over time are not high. Practical value. Monitoring and analysis of the level of air pollution contributes to the detection of dangerous chemicals in the air of a residential area, the patterns of decreasing oxygen content and the dynamics of changes in the content of combustible gases in the air over time and with a change in the distance to the source of pollution. Results. It was found that the air of residential areas does not contain compounds of hydrogen sulfide, carbon monoxide, ammonia, and formaldehyde sufficient for the measurement range of analytical devices. Traces of dust were detected. Recorded decrease in oxygen content indoors and in atmospheric air near roads. The content of combustible gases (LEL) in the studied premises in most samples is higher than outside.

Characterization of harmful gases and bioaerosols of pig farms: a review of the existing literature

From the ecological and economic point of view, intensive pig farming is drawing more and more attention due to the appearance of harmful substances, which significantly increases the risks of air, water and soil pollution. In addition, the optimal criteria for the content of harmful gases, dust and microorganisms in the environment of pig farms are currently not clearly defined. In order to better understand the interaction of harmful emissions and aerosols, the article reviews data from the literature on their main components, concentrations, and interactions. The results showed that the main pollutants in pig farming are harmful gases (ammonia (NH3), carbon dioxide (CO2), methane (CH4), nitrogen oxide (N2O), hydrogen sulfide (H2S), which are released from manure. The presence of even small concentrations of harmful gases in farm air can cause respiratory and cardiovascular system disorders not only in animals, but also in humans. Along with harmful gases, it is important to control the emission of solid particles and dust from livestock premises, which can form aerosols. Microbial aerosols in pig houses contain bacteria, fungi, viruses, which mainly come from the animals themselves, manure or service personnel. The bioaerosol includes the Firmicutes and Bacteroidetes bacterial divisions and their derivatives. In addition, the content of potentially dangerous bacteria in the bioaerosol can reach up to 40 %. A major concern causes the presence of a large number of antibiotic-resistant microorganisms in the air of these farms (including MRSA). The existing strategies and methods to combat these problems are still imperfect and need to be refined. Currently, the interaction of harmful gases, dust, solid particles and microorganisms is not taken into account, which can increase the toxic effect of each other on the animal's body. Therefore, there is a need for a better understanding of these interactions in order to improve the strategy for improving the microclimate conditions by correcting the microbiota, finding and developing biological preparations that contain natural bacteria capable of neutralizing odors and disinfecting livestock premises.

Water quality management of micro swamp wetland based on the “source-transfer-sink” theory: A case study of Momoge Swamp Wetland in Songnen Plain, China

Swamp wetlands play a pivotal role in conserving water sources, regulating river runoff, and maintaining water safety and balance. The study pioneers a new research framework in the field of pollution ecology of swamp wetlands, with a focus on the complex “source-transfer-sink” processes affecting water quality in micro swamp wetlands, an area previously under-researched. Taking the Momoge Wetland in the Songnen Plain as an example, we innovatively combined the “source-transfer-sink” theory with landscape patterns, topographic factors, and their impacts on wetland water quality. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), our research quantifies and models the relationships among various environmental factors. This approach reveals how landscape patterns influence pollutant migration and water quality at a micro-scale. Our findings suggest that a 700-m buffer zone is most effective in capturing the fluctuations in water quality in arid wetlands, which is crucial for wetland management practices. We identified the main pollution sources in the Momoge Wetland, including sediment (21.60%), vegetation (24.94%), livestock and crop-related non-point sources (41.21%), and hydrodynamic factors such as water diversion (12.25%). In pollutant migration, land use emerged as the most significant factor affecting water quality (56.64%), followed by landscape patterns (25.54%). Notably, our study finds that grasslands are essential for water purification, and a uniform distribution of landscape patches significantly improves water quality. This research marks a significant stride in understanding and optimizing landscape patterns to enhance water quality in micro swamp wetlands. The insights gained are invaluable for the protection of these ecosystems and promoting their ecological value functions, providing a reference point for future wetland conservation and management strategies.

Derivation of human health and odor risk control values for soil ammonia nitrogen by incorporating solid-liquid partitioning, ammonium/ammonia equilibrium: A case study of a retired nitrogen fertilizer site in China

Nitrogen fertilizer supports agricultural intensification, but its manufacturing results in substantial contaminated sites. Ammonia nitrogen is the main specific pollutant in retired nitrogen fertilizer sites with potential human health and odor risks. However, few studies focus on ammonia nitrogen risk assessment at contaminated sites, particularly considering its solid-liquid partitioning process (Kd) and ammonium/ammonia equilibrium process (R) in the soil. This study took a closed nitrogen fertilizer factory site as an example and innovatively introduced Kd and R to scientifically assess the human health and odor risk of ammonia nitrogen. The risk control values (RCVs) of ammonia nitrogen based on human health and odor risk were also derived. The maximum concentration of ammonia nitrogen was 3380 mg/kg in the unsaturated soil, which was acceptable for human health because the health RCVs were 5589 ∼ 137,471 mg/kg in various scenarios. However, odor risk was unacceptable for RCVs were 296 ∼ 1111 mg/kg under excavation scenarios and 1118 ∼ 35,979 mg/kg under non-excavation scenarios. Of particular concern, introducing Kd and R in calculation increased the human health and odor RCVs by up to 27.92 times. Despite the advancements in ammonia risk assessment due to the introduction of Kd and R, odor risk during excavation remains a vital issue. These findings inform a more scientific assessment of soil ammonia risk at contaminated sites and provide valuable insights for the management and redevelopment of abandoned nitrogen fertilizer plant sites.

Bandgap engineering of carbon nitride by formic acid assisted thermal treatment for photocatalytic degradation of tetracycline hydrochloride

Carbon nitride had unique structure and electronic properties, and had wide application prospects in the field of photocatalysis. However, the rapid photogenerated carrier recombination efficiency and low photocatalytic activity limit its use. Defect construction was considered to be an effective strategy to improve the photocatalytic activity of carbon nitride. In this paper, nitrogen defective carbon nitride was prepared by formic acid assisted pyrolysis. The formic acid was used to regulate the top atoms and effectively shorten the band gap. Second, the gas released during pyrolysis contributed to the construction of porous structures. SEM, TEM, BET, XPS were used to confirm the presence of nitrogen defective sites. When it was used to photocatalyze the degradation of tetracycline, the main antibiotic pollutant in Dongting Lake, the degradation rate of 95.1 % was achieved in 60 min. Compared with conventional carbon nitride, the degradation rate constants were 0.04751 min−1 and 0.02110 min−1, respectively. Compared with CN, the degradation rate constant of FCN-5 was increased 2.2 times. The photocatalytic degradation activity was verified by photochemical experiments. •O2– and 1O2 active radicals dominate the photocatalytic degradation process. The ecological safety of Dongting Lake water samples repaired by defective carbon nitride has been confirmed, and the toxicity of antibiotics to the germination and growth of legumes has been effectively alleviated. Five cycles of the experiments ensured that the material can be reused. The above work provided reference and basis for the development and design of defective carbon nitride for water body restoration in Dongting Lake basin.

Estimation of the main air pollutants from different biomasses under combustion atmospheres by artificial neural networks

The production of biofuels to be used as bioenergy under combustion processes generates some gaseous emissions (CO, CO2, NOx, SOx, and other pollutants), affecting living organisms and requiring careful assessments. However, obtaining such information experimentally for data evaluation is costly and time-consuming and its in situ obtaining for regional biomasses (e.g., those from Northeast Brazil (NEB) is still a major challenge. This paper reports on the application of artificial neural networks (ANNs) for the prediction of the main air pollutants (CO, CO2, NO, and SO2) produced during the direct biomass combustion (N2/O2:80/20%) with the use of ultimate analysis (carbon, hydrogen, nitrogen, sulfur, and oxygen). 116 worldwide biomasses were used as input data, which is a relevant alternative to overcome the lack of experimental resources in NEB and obtain such information. Cross-validation was conducted with k-fold to optimize the ANNs and performance was analyzed with the use of statistical errors for accuracy assessments. The results showed an acceptable statistical performance for all architectures of ANNs, with 0.001–12.41% MAPE, 0.001–5.82 mg Nm−3 MAE, and 0.03–52.30 mg Nm−3 RMSE, highlighting the high precision of the emissions studied. On average, the differences between predicted and real values for CO, CO2, NO, and SO2 emissions from NEB biomasses were approximately 0.01%, 10−6%, 0.14%, and 0.05%, respectively. Pearson coefficient provided consistent results of concentration of the ultimate analysis in relation to the emissions studied and effectiveness of the test set in the developed models.

Analysing Plastic Pollution: A Case Study of Coastal Pollution from Inland Urban Waterways in Galle City, Sri Lanka

The coastal stretch of Galle city is currently facing an acute pollution with the aggregation of various types of waste on beaches due to malpractices of the community living around the coastal as well as inland areas of the country. Rivers or canals play a key role in the transport of wastes from source to sea and are estimated to emit around 1 metric ton of plastics into the ocean annually. Mainly five sea outfalls are existing along the Galle coastal zone and those transfer various waste towards the beaches. The present case study aims to identify the coastal pollution caused by inland waterways in Galle municipal council area and to detect the main plastic pollutants flow into coastal zone through inland waterways from month of April to June 2023. The waste trap constructed at the main inland waterway located at Galle municipal council area, the Moda Ela trapped different types of waste and the CCTV camera fixed directing to the above-mentioned canal, captures images, uploaded to a cloud system and feed the data to an Artificial Intelligence model to detect the litter type and their counts which flows along the canal daily and get recorded in the smart plastic litter monitoring web-based system using feature extraction techniques in AI. This smart system has mainly identified the count of plastic fragments, plastic bottles, Styrofoam, and trash bags separately, as the main pollutants of this canal and has interpreted graphically and numerically in the smart system which can be freely accessed. Weights of waste were taken manually. Finally, recorded counts of waste in the smart system emphasis that most abundant causative agent affected on pollution of Moda Ela was plastic bottles (58%) while effect of Styrofoam and trash bags were 34% and 8% respectively. Further, average weight of plastic bottles was recorded highest during the study period (130 kg) when compared to other waste categories. According to the results, plastic bottles have become the main source of pollution comes through inland canals which contribute to coastal pollution. Visual observations further convince that the highest pollution source was Moda Ela while less contribution was from Mahamodara canal. The present study highlights plastics as the main pollutants in inland waterways which effect on coastal pollution. Therefore, it is necessary to identify the polluters and need to implement proper rules and regulations while suggesting any alternatives for plastics.
 
 Keywords: Coastal pollution, Inland waterways, Waste trap, Galle Municipal Council area, Artificial intelligence technology