Complex Chemical Processes Research Articles

OVERVIEW OF ROOT ZONE TECHNOLOGY

Central Pollution Control Board (2007) study found that discharge of untreated sewage is sthe single most important cause for surface and ground water pollution n India. There is a large gap between the generation and treatment of domestic wastewater in India. The problem is not only that India lacks sufficient treatment capacity but also that the existing sewage treatment plants are not operated and are not maintained. Most of the government-owned sewage treatment plants remain closed due to improper design, poor maintenance, or lack of reliable electricity supply to operate the plants, absentee employees, and poor management. The wastewater generated in these areas normally percolates in the soil or evaporates. The uncollected wastes accumulate in the urban areas cause unhygienic conditions and release pollutants that leach to surface and groundwater. Moreover, where these treatment plants exist, the cost of treatment is so high that industrialists favour not treating the waste generated and directly dumping the untreated waste into the rivers.The Root Zone Treatment system (RZTS) also known as the reed bed system or constructed wetland system is a sealed filter bed consisting of a sand/gravel/ soil system, occasionally with a cohesive element, planted with vegetation that can grow in wetlands. After removing coarse and floating material, the wastewater passes through the filter bed where biodegradation of the wastewater occurs. The functional mechanisms in the soil matrix responsible for the mineralization of biodegradable matter are characterized by complex physical, chemical and biological processes resulting from the combined effects of the filter bed material, wetland plants, micro-organisms and wastewater. The root zone treatment system also known as the reed bed or constructed wetland system is a sealed filter bed consisting of a sand /gravel/soil system.

Coupling of multicomponent transport models in particle-resolved fluid-solid simulations

An accurate and self-consistent methodology for mass transport of multi-component mixtures in multi phase media is a necessity for a proper description of complex physical and chemical processes in reactors such as catalytic packed beds. In this regard, a novel methodology has been developed to describe and couple underlying transport phenomena in fluid and porous media as well as at the solid-fluid interface. The methodology is symmetric as it treats all components in a mixture equally. The Maxwell-Stefan equations are symmetrically formulated, discretized conservatively and coupled with a compressible flow solver for the fluid part. The Dusty Gas Model is applied inside porous media by developing a self-consistent and robust numerical formulation. A ghost-cell Immersed Boundary Method is used to capture the physics at the solid-fluid interface with the implementation of a novel symmetric non-singular mass flux formulation. Several test cases are established to demonstrate the accuracy and robustness of the newly developed symmetric methodology in this paper. These test cases can be used as benchmark for the future development of symmetric methodologies for multicomponent systems in multi phase media.

Efficient mineralisation and disinfection of neonicotinoid pesticides with unique ZnAl-LDH intercalation structure and synergistic effect of Cu2O crystalline surface

Photocatalysts based on crystallographic effects and visible light-driven tunable nanostructures have been confirmed as a novel, scalable, and easy-to-implement technology. The hydrothermal reaction serves as the main method of synthesis for its novelty, becoming a viable alternative to other complex physical and chemical processes. In this study, hydrothermal conditions' parameters were set for nanostructured Cu2O/ZnAl-LDH photocatalysts. The nanostructured I-scheme Cu2O/ZnAl-LDH photocatalysts were prepared with an intercalated morphology. The prepared photocatalysts became the most effective photocatalyst for mineralizing and degrading nitenpyram solution, achieved nearly complete degradation and exceptionally high mineralization values (>40%) after 210 min of reaction, and exerted some ex vivo antibacterial, sterilising effects and potential for treating waste leachate (removal = 78%). The synergistic effect of Cu2O/ZnAl-LDH with both crystalline surface effect and intercalation structure can lead to reusability, high stability, versatility, and excellent photocatalytic performance in mineralize neonicotinoid pesticides.

Physical Graph-Based Spatiotemporal Fusion Approach for Process Fault Diagnosis.

The rapid development of big data technology and machine learning has increasingly focused attention on fault diagnosis in complex chemical processes. However, data-driven approaches often overlook the inherent physical correlations within the system and lack a robust mechanism for providing trusted explanations for fault diagnosis. To address this challenge, a graph-based fault diagnosis model framework is proposed along with a dependable fault node diagnosis analysis method. In order to enhance the extraction of chemical process features from a spatial perspective, a graph convolution network (GCN)-based node spatial encoding module is integrated. The construction of the adjacency matrix involves combining a priori knowledge of chemical processes with Pearson correlation, thereby incorporating the physical correlations between nodes. Simultaneously, to capture temporal dependencies in fault data, a spatiotemporal feature fusion module based on the long short-term memory network (LSTM) is employed. In terms of model training, a dual-supervision strategy is adopted to ensure stable convergence of the multiclass fault diagnosis model. For model inference, a multi-model voting strategy is designed to mitigate accuracy degradation resulting from model prediction bias. To tackle the interpretability challenge, a fault diagnosis analysis method based on node masking is designed, effectively identifying critical nodes contributing to system faults. Experimental validation on the Tennessee Eastman process demonstrates the effectiveness of the proposed model, achieving high accuracy in fault diagnosis. The average fault diagnosis rate for all fault types reaches 0.9844, showcasing state-of-the-art performance in fault diagnosis.

Node-Aligned Graph-to-Graph: Elevating Template-free Deep Learning Approaches in Single-Step Retrosynthesis.

Single-step retrosynthesis in organic chemistry increasingly benefits from deep learning (DL) techniques in computer-aided synthesis design. While template-free DL models are flexible and promising for retrosynthesis prediction, they often ignore vital 2D molecular information and struggle with atom alignment for node generation, resulting in lower performance compared to the template-based and semi-template-based methods. To address these issues, we introduce node-aligned graph-to-graph (NAG2G), a transformer-based template-free DL model. NAG2G combines 2D molecular graphs and 3D conformations to retain comprehensive molecular details and incorporates product-reactant atom mapping through node alignment, which determines the order of the node-by-node graph outputs process in an autoregressive manner. Through rigorous benchmarking and detailed case studies, we have demonstrated that NAG2G stands out with its remarkable predictive accuracy on the expansive data sets of USPTO-50k and USPTO-FULL. Moreover, the model's practical utility is underscored by its successful prediction of synthesis pathways for multiple drug candidate molecules. This proves not only NAG2G's robustness but also its potential to revolutionize the prediction of complex chemical synthesis processes for future synthetic route design tasks.

Collective Variable-Based Enhanced Sampling: From Human Learning to Machine Learning.

Enhanced-sampling algorithms relying on collective variables (CVs) are extensively employed to study complex (bio)chemical processes that are not amenable to brute-force molecular simulations. The selection of appropriate CVs characterizing the slow movement modes is of paramount importance for reliable and efficient enhanced-sampling simulations. In this Perspective, we first review the application and limitations of CVs obtained from chemical and geometrical intuition. We also introduce path-sampling algorithms, which can identify path-like CVs in a high-dimensional free-energy space. Machine-learning algorithms offer a viable approach to finding suitable CVs by analyzing trajectories from preliminary simulations. We discuss both the performance of machine-learning-derived CVs in enhanced-sampling simulations of experimental models and the challenges involved in applying these CVs to realistic, complex molecular assemblies. Moreover, we provide a prospective view of the potential advancements of machine-learning algorithms for the development of CVs in the field of enhanced-sampling simulations.

Disentangling CO Chemistry in a Protoplanetary Disk Using Explanatory Machine-learning Techniques

Molecular abundances in protoplanetary disks are highly sensitive to the local physical conditions, including gas temperature, gas density, radiation field, and dust properties. Often multiple factors are intertwined, impacting the abundances of both simple and complex species. We present a new approach to understanding these chemical and physical interdependencies using machine learning. Specifically, we explore the case of CO modeled under the conditions of a generic disk and build an explanatory regression model to study the dependence of CO spatial density on the gas density, gas temperature, cosmic-ray ionization rate, X-ray ionization rate, and UV flux. Our findings indicate that combinations of parameters play a surprisingly powerful role in regulating CO abundance compared to any singular physical parameter. Moreover, in general we find the conditions in the disk are destructive toward CO. CO depletion is further enhanced in an increased cosmic-ray environment and in disks with higher initial C/O ratios. These dependencies uncovered by our new approach are consistent with previous studies, which are more modeling intensive and computationally expensive. Our work thus shows that machine learning can be a powerful tool not only for creating efficient predictive models, but also for enabling a deeper understanding of complex chemical processes.

Microplastics in landfill leachate - characteristics and common methods of identification

According to estimates, around 40 % of the world's plastic waste production is buried in landfills, which are still a widely used approach for waste disposal. Waste undergoes a number of physical, chemical, and biological changes after being landfilled, producing landfill leachate, a highly contaminated effluent. These complex physical, chemical, and biological processes that occur in landfills also cause plastics to break into smaller fragments called microplastics (MPs) which are accumulated in landfill leachate due to their small size. Depending on waste type, the degree of degradation, the climate, the characteristics of the landfill sites, socioeconomic factors, and the applied landfilling technology, leachate composition differs between landfills. Microplastics are now emerging particle anthropogenic contaminants, and their study is generating more and more attention from the scientific community and the general public. Even though landfills have taken steps to decrease the severity of this problem, MPs continue to be produced by plastics in both current and former landfills. In this way, humans and biota may be adversely affected by landfill leachate that has been released into the environment. In order to comprehend the patterns of microplastics degradation and the most typical forms of polymers that they contain, identification and characterization of MPs from landfills is highly required. These efforts will contribute to a better understanding of how MPs from landfills affect the environment.

The warming-up effects of quantum-dot light emitting diodes: A reversible stability issue related to shell traps.

The aging phenomenon is commonly observed in quantum-dot light emitting diodes (QLEDs), involving complex chemical or physical processes. Resolving the underlying mechanism of these aging issues is crucial to deliver reliable electroluminescent devices in future display applications. Here, we report a reversible positive aging phenomenon that the device brightness and efficiency significantly improve after device operation, but recover to initial states after long-time storage or mild heat treatment, which can be termed as warming-up effects. Steady and transient equivalent circuit analysis suggest that the radiative recombination current dramatically increases but electron leakage from the quantum dots (QDs) to hole transport layer becomes more accessible during the warming-up process. Further analysis discloses that the notable enhancement of device efficiency can be ascribed to the filling of shell traps in gradient alloyed QDs. This work reveals a distinct positive aging phenomenon featured with reversibility, and further guidelines would be provided to achieve stable QLED devices in real display applications.

Statistical Process Control and Capability Six-pack for Conductivity Measurement in Medicinal Chemical Industry

Statistical Process Control (SPC) is indispensable in the medicinal chemical industry for maintaining pharmaceutical product quality and consistency. Background: With complex chemical processes and stringent regulatory standards, robust quality control measures are essential. SPC enables monitoring and control of key process parameters, identification and correction of process variations, and ensuring final product compliance with specifications. This study evaluates the quality and capability of a process measuring medicinal raw material quality from an international chemical manufacturing company. Methods: Data collected from the conductivity measurement process were analyzed using Minitab software. The process specification was fixed at 30 µS·cm-1 as a maximum permissible limit, with 33 subgroups of size one. The computation of the capability six-pack and analysis generated six plots providing insights into process stability, recent behavior, and capability against specifications, distribution verification and capability indices. Minitab® version 17.1.0 software was used to perform a process capability six-pack analysis, including various charts and statistics to assess process stability and capability. Results: The results indicate that while the process is stable, it lacks capability, with variations exceeding acceptable limit. The process does not meet the maximum allowable specification value, with deviations from normal distribution and following logistic dispersion pattern. Conclusion: The study's novelty lies in its comprehensive yet concise presentation of key metrics essential for evaluating process capability and performance, providing insights for process improvement and quality control. Further investigation and improvement actions are needed to address sources of variation and enhance process capability and performance.

Characterization of Synthetic Brushite Material Precipitated at Room Temperature Developed for Kidney Stone Treatment

Studies on Brushite, which was initially considered as a mineral found in caves, started when it was seen in kidney stones, and finally its applications continued. These studies were focused on synthetically obtaining Brushite. The biggest difficulty in obtaining Brushite synthetically is the execution of complex chemical and sequential processes at high temperatures. Obtaining a stable crystal structure at high temperatures brings another challenge. In this study, for the first time in the literature, the synthesis of Brushite at room temperature by using simple electrolyte, characterization of the precipitates with different methods, and the details of their electrochemical and mechanical behaviour were shared with the readers. Brushite was precipitated on the X2CrNiMo17‐12‐2 materials by using electrolytic method. In the characterization of Brushite deposits, energy dispersive X‐ray microanalysis (EDS), X‐ray diffraction (XRD), Fourier transform infrared (FTIR), scanning electron microscope (SEM), and optical microscope with image analyser were used. Dynamic ultramicro hardness, scratch, and friction‐wear testing equipment were used in mechanical analyses. It has been determined that the electrochemical decomposition of the Brushite material, which was successfully synthesized at room temperature, is quite difficult, and a mechanical load of 10 mN has been defined to be the critical value.

Constitutional adaptation to pKa modulation by remote ester hydrolysis.

The mechanisms through which environmental conditions affect the expression of interconnected species is a key step to comprehending the principles underlying complex chemical processes. In Nature, chemical modifications triggered by the environment have a major impact on the structure and function of biomolecules and regulate different reaction pathways. Yet, minimalistic artificial systems implementing related adaptation behaviours remain barely explored. The hydrolysis of amino acid methyl esters to their corresponding amino acids leads to a drastic change in pKa (ca. 7 and 9, respectively) that protonates the free amino group at physiological conditions. Dynamic covalent libraries (DCvLs) based on amino acid methyl esters and aldehydes respond to such hydrolysis and lead to constitutional adaptation. Each of the libraries studied experiences a DCvL conversion allowing for constituent selection due to the silencing of the zwitterionic amino acids towards imine formation. The selective action of different enzymes on the DCvLs results in states with simplified constitutional distributions and transient chirality. When additional components (i.e., scavengers) that are not affected by hydrolysis are introduced into the dynamic libraries, the amino acid methyl ester hydrolysis induces the up-regulation of the constituents made of these scavenging components. In these systems, the constituent distribution is resolved from a scrambled mixture of imines to a state characterized by the predominance of a single aldimine. Remarkably, although the final libraries display higher "simplexity", the different transient states present an increased complexity that allows for the emergence of organized structures [micelle formation] and distributions [up-regulation of two antagonistic constituents]. This reactive site inhibition by a remote chemical modification resembles the scenario found in some enzymes for the regulation of their activity through proximal post-translational modifications.

A multiscale adaptive framework based on convolutional neural network: Application to fluid catalytic cracking product yield prediction

Since chemical processes are highly non-linear and multiscale, it is vital to deeply mine the multiscale coupling relationships embedded in the massive process data for the prediction and anomaly tracing of crucial process parameters and production indicators. While the integrated method of adaptive signal decomposition combined with time series models could effectively predict process variables, it does have limitations in capturing the high-frequency detail of the operation state when applied to complex chemical processes. In light of this, a novel Multiscale Multi-radius Multi-step Convolutional Neural Network (MsrtNet) is proposed for mining spatiotemporal multiscale information. First, the industrial data from the Fluid Catalytic Cracking (FCC) process decomposition using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) extract the multi-energy scale information of the feature subset. Then, convolution kernels with varying stride and padding structures are established to decouple the long-period operation process information encapsulated within the multi-energy scale data. Finally, a reconciliation network is trained to reconstruct the multiscale prediction results and obtain the final output. MsrtNet is initially assessed for its capability to untangle the spatiotemporal multiscale relationships among variables in the Tennessee Eastman Process (TEP). Subsequently, the performance of MsrtNet is evaluated in predicting product yield for a 2.80 × 106 t/a FCC unit, taking diesel and gasoline yield as examples. In conclusion, MsrtNet can decouple and effectively extract spatiotemporal multiscale information from chemical process data and achieve a approximately reduction of 30% in prediction error compared to other time-series models. Furthermore, its robustness and transferability underscore its promising potential for broader applications.

Combined flow, temperature and soot investigation in oxy-fuel biomass combustion under varying oxygen concentrations using laser-optical diagnostics

In pulverized oxy-fuel biomass combustion, intricate interaction of complex fluid-mechanical, particle-dynamical, and chemical processes manifests even at intermediate scales. This study performs a comprehensive analysis aimed to understand the processes of flame stabilization, pollutant formation, and combustion behavior of biomass particles, while evaluating the impact of varying oxygen concentration on combustion. The investigation is conducted with a comprehensive data set, encompassing various oxy-fuel operation conditions ranging from 27vol.% to 36vol.% in O2 concentration, along a comparable air flame. These conditions are realized in an optically accessible gas-assisted solid fuel combustor designed to generate flames up to 70kWth. Advanced laser-optical diagnostics are employed to capture the combustion process. These techniques include the use of two-phase PIV/PTV for simultaneous measurements of flow and particle velocities, the application of tomographic absorption spectroscopy to elucidate three-dimensional gas temperature distributions, and the establishment of a quasi-simultaneous LII and LIF experimental setup to capture both PAH and soot occurrences. Additionally, chemiluminescence imaging of CH∗ is incorporated. The analysis extends to both single-phase and two-phase combustion, revealing a pronounced influence of oxygen concentration on both regimes. The inner recirculation zone, characteristic of a swirl flame, decreases with increasing oxygen content, coupled with an acceleration of the main flow downwards due to heightened heat release proximate to the nozzle. In two-phase combustion, a significant combustion delay is observed compared to single-phase combustion. Furthermore, the occurrences of PAHs and soot are clearly separated, and a pronounced influence of local gas composition on soot formation is evident. Comparative examination between oxy-fuel and air combustion show that an oxy-fuel condition with an oxygen concentration just below 33vol.% closely mirrors the flame characteristics of air combustion.

A Study of Chemical Processes of Nitrate in Atmospheric Aerosol and Snow Based on Stable Isotopes

Nitrate (NO3−) is a prominent atmospheric pollutant and a key chemical constituent of snow and ice, which plays a crucial role in the atmosphere and significantly impacts regional climate and environment conditions through a series of complex chemical processes. By summarizing the recent research progress on the nitrate chemical process (particularly on the isotopic measurements of NO3− (δ15N, Δ17O and δ18O)) in atmosphere and glacier snow, this study mainly investigated the chemical compositions and chemical processes, formation pathways, and photochemical reactions of nitrate in snow and atmosphere. Our results identified that the main ways of atmospheric nitrate formation are the hydrolysis of N2O5 and the reaction of ·OH with NO2; the spatial distribution of Δ17O and δ18O values of atmospheric nitrate have a significant latitudinal trend between 30° N–60° N; the study of stable isotopes (δ15N and δ18O) and the oxygen isotope anomaly (Δ17O) of nitrate have mainly been carried out over the densely populated and coastal mega cities; there exist significant gaps in the study of chemistry processes of nitrate in snow and ice and the air–snow interfaces across glaciated regions. This study provides a basic reference for more robust observations and research of nitrate in glacier areas in the future.

Accelerating operation optimization of complex chemical processes: A novel framework integrating artificial neural network and mixed-integer linear programming

We present an automated framework that integrates rectified linear unit activated artificial neural network (ReLU-ANN) and mixed-integer linear programming (MILP) to enable efficient operational-level optimization of complex chemical processes. Initially, data is generated through rigorous simulations to pre-train surrogate models based on ReLU-ANN (classification and regression), and subsequently, MILP is employed for optimization by linearly formulating these models. This novel framework efficiently handles complex convergence constraints through a classification neural network which will be used for high-throughput screening data for regression, while simultaneously implementing an 'optimizing while learning' strategy. By iteratively updating the neural network based on optimization feedback, our approach streamlines the optimization process and ensure the feasibility of optimum solution. To demonstrate the versatility and robustness of our proposed framework, we examine three representative chemical processes: extractive distillation, organic Rankine cycle, and methanol synthesis. Our results reveal the framework’s potential in enhancing optimization effect while concurrently reducing computational time, surpassing the capabilities of typical optimization algorithms. As for the three processes, optimization effectiveness improved by 10.11%, 28.69%, and 5.45%, respectively, while execution time were reduced by 71.71%, 54.49%, and 59.38%. This notable enhancement in optimization efficiency stems from a substantial reduction in costly while ineffective objective function evaluations. By seamless integration of ReLU-ANN and MILP, our proposed framework holds promise for improving the optimization of complex chemical processes, yielding superior results within significantly reduced timeframes compared to traditional approaches.

Multimodal Operando Analysis of High-Capacity Electrodes with Photons and Neutrons

High-capacity electrodes such as sulfur cathodes and silicon anodes are promising candidates for sustainable electrochemical energy storage in a post-lithium-ion era. While lithium-sulfur (Li/S) batteries have five times higher theoretical gravimetric energy density (about 2500 Wh/kg) than modern lithium-ion batteries [1], silicon is an interesting anode material for lithium-ion batteries because its specific capacity is ten times higher than that of commercially used graphite anodes. However, despite decades of research, the sharp drop in capacity with the increasing number of cycles is still a major obstacle to widespread technical use of both electrodes. Operando techniques [2,3] are well suited to gaining a mechanistic understanding of the degradation processes. In particular, the simultaneous combination of several independent measurements (multimodal) during the operation of the electrodes allows deep insights into the degradation mechanisms. It enables a more advanced mechanistic understanding of the two complex chemical processes.This conference contribution presents the results of novel experimental setups in which multiple independent measurements were performed simultaneously. Electrochemical impedance spectroscopy (EIS), temperature measurement and X-ray imaging or neutron techniques such as neutron small angle scattering and neutron reflectometry were performed over several cycles. Simultaneously, cells were charged and discharged galvanostatically or potentiostatically. Structural changes at the macroscopic and microscopic levels and chemical compositions can be correlated with characteristic signals in the EIS and charge-discharge curves.In particular, imaging and spectroscopic techniques on button cells with X-rays, especially at a synchrotron source, allowed essential insights into the processes in lithium/sulfur batteries [4–8]. But also, applying this characterization method on industrially relevant pouch cells could provide important information about processes in this cell format [9].Neutron reflectometry is particularly well suited for analyzing electrochemical interfaces on silicon electrodes [10], which was also performed in operando mode [11–13]. Furthermore, for this electrode material, important insights into electrode processes could be obtained using operando X-ray spectroscopy and X-ray imaging at the synchrotron [14,15].

MATHEMATICAL MODELS OF CHEMICAL PROCESSES WITH VARIABLE CHARACTERISTICS

The article discusses the construction of mathematical models of complex chemical processes when different factors act on the result of a chemical reaction at different points in time. The method of least squares is used. This material can be used to teach university students in the disciplines of Mathematical Statistics, Numerical Methods, General Technologies of Food Production, Food Chemistry, etc. Applications from the analysis of experimental research models and laboratory testing of innovative technologies for the development of different types of galusa can be supported by incoming students in the chemical engineering program. Examples from the analysis of experimental research models of laboratory tests of new acid components for a carbonate collector can be applied in the training of students in the Chemical Engineering educational program.

Impact of Aggregate Grain Size on ASR-Induced Expansion.

Alkali-silica reaction (ASR) is a sequence of complex chemical processes, resulting in the formation of alkali silica gels with high swelling ability. ASR leads to the expansion of concrete and the degradation of its microstructure. The susceptibility of aggregates to alkali reaction depends, among other factors, on the type and origin of the aggregate, the presence of reactive forms of silica, the mineral composition, and the geometric properties of the aggregate, such as shape and grain size. This study aimed to investigate the impact of the grain size of polymineral post-glacial gravel aggregate, originating from the northern regions of Poland, on its susceptibility to ASR. The expansion of mortars made from polymineral aggregate and the cracking of grains and cement matrix due to the occurring reactions were analyzed. Based on the conducted research, it was observed that the expansion of mortars depends on the grain size of the aggregate. It was demonstrated that the fraction of reactive aggregate generating the most significant elongation of mortars is in the range of 1.0-2.0 mm. The reaction of silica with alkalis continued until the depletion of reactive components in the aggregate. The relationship between the progress of corrosive processes and the grain size of the aggregate was evident in the form of different linear elongation increments of mortars over time. The expansion of mortars was caused by the swelling ASR gel, inducing stress in the grain and the surrounding cementitious paste.

Mechanism of Formation of Smog and some Adverse Effect of Smog on Environment and Human Being

Abstract: The formation of smog is a complex chemical process with significant implications for the environment and human health. This phenomenon primarily results from the interplay of various reactants and products, with its origins tracing back to specific sources. The key contributors to photochemical smog include nitrogen oxides, hydrocarbons, and the energy source that drives the reaction—sunlight. Notably, pollutants like nitrogen oxides and hydrocarbons are byproducts of fossil fuel combustion, primarily emitted from coal-fired power plants and various energy production facilities. Additionally, volatile organic compounds (VOCs) are generated when fossil fuels undergo incomplete combustion, as observed in activities like backyard burning and wood-burning stoves. Smog, characterized by a blend of airborne pollutants, poses significant threats to both human well-being and the environment. It can lead to a range of health issues, including respiratory difficulties, asthma exacerbation, heightened susceptibility to lung infections and colds, and eye irritation.