Identification Of Pollution Sources Research Articles

Air quality analysis and modeling in urban area: A review study

Urban areas across the world are experiencing significant challenges related to air quality, with adverse effects on public health, the environment, and overall quality of life. This thesis aims to investigate and model air quality in urban environments, focusing on the identification of pollution sources and the evaluation of potential mitigation strategies. The research begins by analyzing the complex interplay of factors influencing urban air quality, including emissions from vehicular traffic, industrial activities, and meteorological conditions. Various modeling techniques, such as numerical simulations, statistical analyses, and machine learning algorithms, are employed to assess the spatiotemporal distribution of air pollutants within urban areas. The overview is carried out to focus on various parameters such as emission sources, meteorological parameters, and topographical features to predict pollutant concentrations. The study also explores the effectiveness of different mitigation strategies to improve urban air quality. These strategies may include urban planning measures, such as the design of green spaces and pedestrian-friendly zones, as well as transportation-related interventions like the promotion of electric vehicles and the enhancement of public transportation systems. The impact of these strategies on reducing pollutant concentrations is quantitatively assessed using the developed air quality models. Furthermore, the study considers the implications of climate change on urban air quality, acknowledging that changing climate patterns can exacerbate air pollution issues. Potential adaptation strategies to address these challenges are discussed. In conclusion, this thesis offers a comprehensive analysis of air quality modeling in urban areas, providing insights into the sources of pollution and the effectiveness of mitigation measures. The research aims to inform policymakers, urban planners, and environmental stakeholders in their efforts to create healthier and more sustainable urban environments.

An integrated modelling framework for multiple pollution source identification in surface water

Pollution source identification is vital in water safety management. An integrated simulation-optimization modelling framework comprising a process-based hydrodynamic water quality model, artificial neural network surrogate model and particle swarm optimization (PSO) was proposed to achieve rapid, accurate and reliable pollution source identification. In this study, the hydrodynamics and water quality processes in a straight lab-based flume were simulated to test pollution source identification under steady flow conditions. Additionally, the pollution source identification in the unsteady flow conditions was examined using a real-life estuary, specifically the Yangtze River estuary. First, we developed two process-based models to simulate hydrodynamics and water quality in the flume and estuary. Then, the data generated from the process-based models were used to develop surrogate models. Three typical artificial neural networks (ANNs) algorithms: backpropagation (BP), radial basis function (RBF) and general regression neural networks (GRNN) were selected to develop surrogates for process-based models (PBMs), and they were coupled with PSO algorithm to achieve the hybrid modelling framework for pollution source identification. Our results showed that hybrid PBM-ANNs-PSO models could be applied to identify the pollution source and quantify release intensity in spatial distribution when the discharge type was assumed as the point source with a continuous release. Multiple-performance criteria metrics, in terms of the coefficient of determination, root-mean-square error, mean absolute error, evaluated the model performance as “Excellent prediction”. The BP-PSO models consistently appear to be the top-performing source identification model within the developed models, with most cases of relative error (RE) values lower than 5%. The new insights from the hybrid modelling framework would provide useful information for the local government agency to make reasonable decisions regarding pollution source identification issues.

Groundwater pollution source identification using Metropolis-Hasting algorithm combined with Kalman filter algorithm

Increasing the precision of groundwater pollution source identification (GPSI) is crucial for groundwater pollution control and risk management. Bayesian theory based on the Markov Chain Monte Carlo (MCMC) method is a useful strategy of solving the GPSI problem. However, because of the nonlinear and uncertainty characteristics of GPSI, the Metropolis-Hasting (MH) algorithm, one of the most well-known MCMC algorithms, has the disadvantage of relatively low precision and is time-consuming. To address this problem, the Kalman filter (KF) algorithm was combined with the MH algorithm and referred to as the Kalman filter Metropolis-Hasting (KF–MH) algorithm. The algorithm generates a new initial distribution that is close to the true value through a prior distribution, and the new initial distribution is used to perform subsequent iterations of the calculation. The viability and superiority of the proposed KF-MH algorithm were assessed in three hypothetical GPSI cases under different conditions. In the inversion process, a surrogate model was constructed using a temporal convolutional network (TCN) to reduce the computational pressure imposed by the numerical simulation model. The results of the TCN surrogate model in the cases illustrate the high accuracy of the TCN surrogate model in fitting the groundwater numerical model. In the three cases, the normalized errors between the identification results and the true values of the source features obtained with the KF–MH algorithm were significantly lower than those of the MH algorithm. The results indicate that the proposed KF–MH algorithm has higher inversion accuracy than the MH algorithm.

Laboratory observations of solute transport in groundwater basins

This paper introduces a method of groundwater pollution source identification obtained from laboratory by a physical instrument of Flow System Sand-Box Model. A Unit basin of a simple flow system and a Tóthian basin of a hierarchically nested flow structure are introduced as case studies. We add step-input solutes at recharge zones, separately or simultaneously, to obtain the breakthrough curves (BTCs) at each discharge zone. For the Unit basin, its BTC shows a gradual upward trend with a decreasing slope but with two more pronounced steps, which differs a lot from previous results of analytical and numerical solutions. These steps are caused by the settings of the current physical instrument as no solutes are input at the upper two discharge ports. For the Tóthian basin, internal steps may exist due to the late-time arrival of intermediate and regional groundwater flow systems. Although the Unit basin and Tóthian basin can both have internal steps in the BTCs, they are due to different reasons. By jointly solving solute mass balance equations with water mass balance equations, the circulation rate for each flow system is calculated, which quantitatively explains the phenomena encountered in the experiment and highlights the important contributions of intermediate and regional flow systems to the entire groundwater circulation. We indirectly obtain the pulse-input BTCs by deriving the step-input BTCs respective to time, which is also the residence time distribution (RTD) of the groundwater basin, that the groundwater pollution sources can be identified. The pulse-input BTC is characterized by peaks and sloping tails. The peaks correspond to the intermediate and regional groundwater flow systems. The sloping tails indicate the rate at which solutes are flushed out or contaminants fade away. By inputting tap water into the groundwater basin to purify the contaminations, we conduct self-purification experiments. The shapes of the solute-input curves and the self-purification curves are systematically symmetrical. As the self-purification experiment has less external interference, it is possible to use the self-purification curves to characterize and identify the groundwater flow system. This study proposes a physical method for identifying sources of groundwater contamination, and achieves a comprehensive understanding of the law of solute transport in groundwater basins, which could guide the prevention of non-point contamination at the basin scale.

Remote sensing inversion of water quality parameters in the Yellow River Delta

In recent years, with the rapid socio-economic development of the Yellow River Delta (YRD), the pressure on the supply of water resources has continued to rise. The development of oil-based industries has also led to a series of ecological and environmental problems, such as wetland degradation and water quality deterioration. As an increasing number of rivers are getting polluted, resulting in the deterioration of their water quality, monitoring, managing, and protecting water resources in the YRD is particularly important. In this study, water quality monitoring data and simultaneous Sentinel-2 image data from April 30, 2020, to October 26, 2021, were used to construct an experimental sample in the YRD. Water quality parameters (WQPs) concentrations were correlated with Sentinel-2 image element spectral reflectance and sensitive bands were selected. An empirical method based on the characteristic bands was used to invert a total of six water quality indicators, namely dissolved oxygen (DO), permanganate index (CODMn), ammonia nitrogen (NH3-H), total phosphorus (TP), total nitrogen (TN) and turbidity. The results show: (1) A total of five water quality inversion models for DO, TN, CODMn, TP and TN were effective in the areas of the Guangli River, the Tiaohe and the Branch River. The inversion accuracies of the five inversion models (R2of 0.6099, 0.9271, 0.9581, 0.8784 and 0.7387; RMSE of 1.2723, 0.3413, 0.9923, 0.0118 and 1.8476; RPD of 1.53, 2.08, 3.56, 2.76 and 1.53) indicated the feasibility of the water quality inversion method based on Sentinel-2 data using statistical theory for monitoring water quality concentration in the YRD. (2) The spatial distribution of water quality in the YRD was generally characterized by high water quality in the upper reaches and low water quality in the middle and lower reaches (except for some seasonal variations). Among them, the water quality of the upper reaches of the Guangli River was poor, with opposite trends in DO and TN concentrations. In the Tiaohe, CODMn and TP concentrations were not strongly correlated. However, CODMn and TP concentrations were high in the middle reaches where water quality was the worst. The TN concentrations in the Branch River decreased between 2020 and 2021, but the water quality is still in Category V. Therefore, continued attention and appropriate water quality management measures in the YRD are required. Further, by measuring water quality indicators at monitoring stations, regression-fitting equations for WQPs were established to obtain complementary multi-platform observations. Thus, the water quality conditions in the YRD region can be evaluated more accurately and quickly. The research results not only provide an important reference basis for the identification and monitoring of pollution sources, prevention and treatment of water environment pollution in the YRD, but also provide water security for socio-economic and ecological environment security.

Pollution profile, source identification, and risk assessments of persistent organic pollutants (POPs) and toxic elements in the sediments of one of the water supply areas (Sultanbahçedere lake) to Istanbul, Türkiye

This study was done to comprehensively exhibit the pollution level of surface sediments taken from Sultanbahçedere dam lake in terms of some persistent organic pollutants (POPs) and toxic elements, to decide the possible sources of polycyclic aromatic hydrocarbons (PAHs), to perform the ecological and human health risk assessments, and to evaluate the pollution profile with the hierarchically clustered heatmap (HCHM) analysis and principal component analysis (PCA). Naphthalene, Benzo(b)fluoranthene, Fluoranthene, Pyrene for PAHs; p,p-DDE, p,p-DDD, alpha-HCH, p,p-DDT, Endrin for OCPs; hexa and hepta chlorinated congeners accounting for 50.63% and 28.44% for polychlorinated biphenyls (PCBs); 1234678-HpCDD, OCDD for polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs); PCB 118, PCB 105, PCB 167 for dioxin like-PCBs (DL-PCBs) and Al, Fe, Ca for elements were predominant in almost all of the sediments with a few exceptions. S4 site, which is one of the mid-sites of reservoir, has high concentrations in most of these pollutants. According to PAH diagnostic analysis, PAHs in the surface sediments are mostly affected by pyrogenic sources. The concentrations of POPs do not pose any toxic risk to aquatic organisms according to sediment quality guideline (SQG) and the limit values of other countries accepted in the world. Also, since none of the elements exceeded the effect range median (ERM) values, they do not pose a significant toxic risk to aquatic organisms. There is no risk for non-carcinogenic and carcinogenic hazards both for children and for adults considering hazard quotient (HQ), hazard index (HI), and lifetime cancer risk (LCR) values of all pollutants.

Towards hyperlocal source identification of pollutants in cities by combining mobile measurements with atmospheric modeling

While mobile, vehicle-based sampling of air quality has provided insights into pollutant distributions within cities, previous research has been limited in using observed pollution hotspots to identify and localize unknown emission sources away from the road. Such “hyperlocal source identification” requires hotspots identified through mobile sampling to be linked to emission sources by tracing atmospheric transport upwind from observed hotspots. Here we analyze almost one year's worth of pollutant observations from two Google Street View cars driven around the Salt Lake area, with repeated sampling in select neighborhoods. We then present a method to bridge the gap in hyperlocal source identification by using a high-resolution atmospheric transport model, as part of a new Stochastic Lagrangian Monte Carlo (SLMC) method to identify emission sources based on mobile observations. Case studies with both known and unknown sources are presented to illustrate the efficacy of the SLMC method in hyperlocal source identification. With the availability of techniques like SLMC, we envision mobile air quality monitoring vehicles can serve as “roving sentinels” in identifying unknown pollution sources.

Seasonal Variation and Source Identification of PM10 in an Industrialized City

AbstractA 1‐year PM10 sampling campaign from January 2019 to December 2019 is conducted in an urban industrial area Raipur, India. For the development of effective pollution control strategies, quantitative assessment of potentially toxic metals and accurate identification of pollutant sources are essential. The annual average PM10 aerosol mass concentration during the study period is recorded as 187.89 ± 91 µg m−3. PM10 concentrations are found to vary with meteorological parameters such as wind speed, temperature, rainfall, and humidity. Heavy metals including Fe, Mn, Ni, Pb, Zn, Al, Ba, Cr, Sr, Ga, Mg, and V are studied using atomic absorption spectroscopy (AAS). Seasonally, average winter time PM10 concentration (234.86 ± 104.3 µg m−3) is accounted higher as compared to summer (169.02 ± 27.1 µg m−3) and rainy season (106.28 ± 32.6 µg m−3) during the study period. The aerosol sources identified by positive matrix factorization (PMF) model are: iron and steel production (34.7%), vehicular emissions (29.3%), road dust (14.6%), heavy oil/petroleum (14.6%), and coal combustion (6.9%). Among these sources, iron and steel production contributes maximum in PM10 concentrations. The probable source locations are recognized through concentration weighted trajectory (CWT) analysis. The AirQ+ model is used to assess the health effects of regional PM10 concentrations.

Pollution source identification of nitrogen and phosphorus in the lower West Main Canal, the Ganfu Plain irrigation district (South China).

The degradation of surface water quality has been a widespread concern around the world. However, irrigation canal water does not attract much attention although it is important to agriculture and population. In this study, a 5-year water quality monitoring of surface water was conducted in the lower West Main Canal of the Ganfu Plain irrigation district to identify the levels and pollution sources of nitrogen and phosphorus.Over 75% of samples had total phosphorus (TP) concentrations of > 0.02mg/L, and all samples had total nitrogen (TN) concentrations of > 0.2mg/L, indicating a risk of eutrophication. The concentrations of NO3--N and NH4+-N averagely occupied 57% and 18% of TN, respectively. PCA analysis showed that phosphorus and nitrogen in canal water were associated with meteorological factors, urban life and surface runoff, agricultural cultivation, livestock-poultry breeding, and water-sediment interaction in the wet season, whereas they were affected by meteorological factors, industrial effluent, urban domestic sewage, and livestock-poultry breeding in the dry season. Absolute principal component score-multiple linear regression (APCS-MLR) model results revealed that (1) agricultural cultivation plus livestock-poultry breeding contributed 43.2% of TP in canal water in the wet season, while livestock-poultry breeding contributed 52.9% in the dry season, and (2) domestic sewage plus surface runoff contributed 29.4% of TN in the wet season, while livestock-poultry breeding contributed 45.9% in the dry season. The unidentified sources had significant contributions of > 20% for almost all variables. So further investigations are required for determining unidentified sources, and anthropogenic pollution control is imperative for canal water quality protection.

Pollution status and pollution source identification in the groundwater of Yar-Dalla in Wudil, Kano, Nigeria

Pollution status and pollution source identification in the groundwater of Yar-Dalla in Wudil, Kano, Nigeria

Surface Water Pollution Source Identification and Quantification: Literature Review

Surface Water Pollution Source Identification and Quantification: Literature Review

Remote sensing identification of urban water pollution source types using hyperspectral data

Owing to accelerated urbanisation, increased pollutants have degraded urban water quality. Timely identification and control of pollution sources enable relevant departments to effectively perform water treatment and restoration. To achieve this goal, a remote sensing identification method for urban water pollution sources applicable to unmanned aerial vehicle (UAV) hyperspectral images was established. First, seven fluorescent components were obtained through three-dimensional excitation-emission matrix fluorescence spectroscopy of dissolved organic matter (DOM) combined with parallel factor analysis. Based on the hierarchical cluster analysis of the seven fluorescence components and three spectral indices, four pollution source (PS) types were determined, namely, domestic sewage, terrestrial input, agricultural and algal, and industrial wastewater sources. Second, several water colour and optical parameters, including the absorption coefficient of chromophoric DOM at 254 nm, humification index, chlorophyll-a concentration, and hue angle, were utilised to develop an identification method with a recognition accuracy exceeding 70% for the four PSs that is suitable for UAV hyperspectral data. This study demonstrated the potential of identifying PSs by combining the fluorescence characteristics of DOM with the optical properties of water, thus expanding the application of remote sensing technologies and providing more comprehensive and reliable information for urban water quality management.

Groundwater pollution source identification and health risk assessment in the North Anhui Plain, eastern China: Insights from positive matrix factorization and Monte Carlo simulation

Groundwater contaminants from natural and anthropogenic sources pose a serious threat to the ecological environment and public health. In this study, 30 groundwater samples were collected from shallow wells at a large central water source in the North Anhui Plain, eastern China. Hydrogeochemical methods, positive matrix factorization (PMF) model, and Monte Carlo simulation were used to determine the characteristics, sources, and human health risks of inorganic and organic analytes in groundwater. The groundwater was weakly alkaline with high total hardness and was dominated by HCO3-Mg·Ca, HCO3-Ca·Mg, and HCO3-Ca·Mg·Na hydrochemical facies. The concentration of naphthalene was at a safe level, while the concentrations of F−, NO3− and Mn in 16.7 %, 26.7 % and 40 % of the samples, respectively, exceeded threshold risk-based values based on Chinese groundwater quality standards. Hydrogeochemical methods revealed that water-rock interactions (including weathering of silicate minerals, dissolution of carbonates, and cation exchange), acidity, and runoff conditions control the migration and enrichment of these analytes in groundwater. The PMF model indicated that local geogenic processes, hydrogeochemical evolution, agricultural activities, and petroleum-related industrial sources were the main factors affecting groundwater quality, with contributions of 38.2 %, 33.7 %, 17.8 %, and 10.3 %, respectively. A health risk evaluation model based on Monte Carlo simulation indicated that 77.9 % of children were exposed to a total noncarcinogenic risk above safe thresholds, about 3.4 times higher than the risk to adults. The main contributor to human health risk was F− originating from geogenic processes; thus, F− was identified as a priority for control. This study demonstrates the feasibility and reliability of combining source apportionment techniques and health risk assessment to evaluate groundwater quality.

Adjoint models with non-Fickian reactive transport to identify pollutant sources in water

Pollutant source identification (PSI) has remained a research topic in environmental sciences for decades for hazardous chemicals, with the assumption that the transport of pollutants is Fickian. Pollutant transport in natural media, however, is usually non-Fickian, characterized by slow retention or preferential movement, and PSI models remained unknown for non-Fickian, reactive transport. To fill this knowledge gap, an adjoint fractional advection-dispersion-reaction equation (fADRE) was derived in this study to identify the source location of reactive pollutants undergoing non-Fickian diffusion with multiple phases in subsurface water with any dimension and boundary conditions. Six fundamental chemical reactions varying from radioactive decay to bimolecular reactions were considered, whose backward-in-time rate equations were simulated by particle tracking combined with backward non-Fickian diffusion. Applications showed that the adjoint fADRE identified the source location of reactive tracer transport in “homogeneous” sand columns repacked in our laboratory and a heterogeneous alluvial aquifer in an actual field site, while the Fickian-based, classical PSI model overestimated by four times the source location of reactive pollutants undergoing strong non-Fickian transport. To expand the applicability of the adjoint fADRE for PSI, we also discussed the impacts of the immobile pollutant phase and reactive kinetics on PSI, pollutant release history recovery, and usage of PSI for complex chemical reactions. The results showed that the fundamental chemical reactions considered in this study can affect PSI by adjusting the location and value of the peak of the probability density functions under conditions such as a relatively large decay constant or a space-dependent reaction rate, and the immobile pollutants can also affect PSI by shortening the source location and elongating the release time for pollutants usually detected only in the mobile phase. Therefore, non-Fickian dynamics and pollutant phases (which were missed by the adjoint ADRE), as well as reaction kinetics, must be captured by the adjoint model, such as the adjoint fADRE derived in this study, for PSI in real-world geological media.

Bayesian source identification of urban-scale air pollution from point and field concentration measurements

Bayesian source identification of urban-scale air pollution from point and field concentration measurements

Pollution source identification and abatement for water quality sections in Huangshui River basin, China

Accurately obtaining the pollution sources and their contribution rates is the basis for refining watershed management. Although many source analysis methods have been proposed, a systematic framework for watershed management is still lacking, including the complete process of pollution source identification to control. We proposed a framework for identification and abatement of pollutants and applied in the Huangshui River Basin. A newer contaminant flux variation method based on a one-dimensional river water quality model was used to calculate the contribution of pollutants. The contributions of various factors to the over-standard parameters of water quality sections at different spatial and temporal scales were calculated. Based on the calculation results, corresponding pollution abatement projects were developed, and the effectiveness of the projects was evaluated through scenario simulation. Our results showed that the large scale livestock and poultry farms and sewage treatment plants were the largest sources of total nitrogen (TP) in Xiaoxia bridge section, with contribution rates of 46.02% and 36.74%, respectively. Additionally, the largest contribution sources of ammonia nitrogen (NH3–N) were sewage treatment plants (36.17%) and industrial sewage (26.33%). Three towns that contributed the most to TP were Lejiawan Town (14.4%), Ganhetan Town (7.3%) and Handong Hui Nationality town (6.6%), while NH3–N mainly from the Lejiawan Town (15.9%), Xinghai Road Sub-district (12.4%) and Mafang Sub-district (9.5%). Further analysis found that point sources in these towns were the main contributor to TP and NH3–N. Accordingly, we developed abatement projects for point sources. Scenario simulation indicated that the TP and NH3–N could be significantly improved by closing down and upgrading relevant sewage treatment plants and building facilities for large scale livestock and poultry farms. The framework adopted in this study can accurately identify pollution sources and evaluate the effectiveness of pollution abatement projects, which is conducive to the refined water environment management.

A review of water quality models and monitoring methods for capabilities of pollutant source identification, classification, and transport simulation

A review of water quality models and monitoring methods for capabilities of pollutant source identification, classification, and transport simulation

Deriving analytical expressions of the spatial information entropy index on riverine water quality dynamics

Information entropy theory has been largely applied in hydrology and water resource engineering. Recently, entropy theory has received significant attention for describing reactive solute mixing and transport processes in ground water and surface water and for its applications in water quality management. However, analytical expressions of the entropy index of riverine water quality dynamics are often not included in the literature. In this study, the analytical expressions of the spatial information entropy index (Gx) in the 1D steady-state transport process under full-extent observation and tracking observations are derived after the definition of the system boundary and probability space. The expressions of Gx were successfully validated by field data from two tracer experiments, and the different characteristics of Gx under different riverine hydraulic situations were uncovered. The formula of Gx shows a reasonable linkage with the Peclet number (Pe) in hydrodynamics. This indicates that Gx is a physical quantity describing or linking to the real world more than a statistical index, and in practice, similar decision or management measurements based on entropy theory can be made for rivers with similar Pe conditions. Hydrologists will benefit in many ways from analytical expressions, such as theoretical analysis, quick calculation of the entropy index, preliminary engineering design, etc. A clearly defined example of maximum entropy time (tGmax) and its application in water quality evaluation, monitoring optimization and pollution source identification are demonstrated. These new findings will provide a convenient tool for catchment water quality management.

Source identification of trace elements in groundwater combining APCS-MLR with geographical detector

Accurately identifying the sources of trace elements in groundwater is difficult, especially in the areas with intensive human activities. This study proposes a framework to identify and quantify the contributions of potential sources of trace elements in groundwater. Health risk assessment is first conducted to identify the trace elements with the highest threats to groundwater quality, and then the geographical detector is used to qualitatively investigate the potential sources and processes dominating the pollution from identified trace elements, and the absolute principal component scores-multivariate linear regression (APCS-MLR) is used to quantify the contributions of different sources to the accumulation of trace elements. The proposed approach was applied to China’s Loess Plateau, a typical water-limited area with rapid urbanization and intensive agricultural activities, after conducting two groundwater sampling campaigns in a flood and a dry season. The trace element concentrations in groundwater exhibited significant spatial and seasonal differences. Health risk assessment revealed that Cr and As through oral ingestion were responsible for non-carcinogenic and carcinogenic risks in different seasons. Land use was the primary factor influencing the spatial heterogeneity of Cr in the flood season, while elevation and distance from city were dominant factors in the dry season. Normalized difference vegetation index (NDVI) and distance from city contributed the most to As change in the flood season, but NDVI was the major contributor in the dry season. The average contributions of potential pollution sources to trace elements in groundwater were in the order of natural source (35%) > geogenic process (17%) > industrial source (13%) in the flood season, but mixed sources (47%) > agricultural activities (18%) in the dry season. Distinguished from the traditional source apportionment methods, the newly proposed approach is not limited to identification of pollutant sources, but also can obtain the exact contributions of different sources. The findings provide useful information for groundwater management in the Loess Plateau.

A study on the analysis of dynamical transmission behavior and mining key monitoring stations in PM and O3 networks in the Beijing-Tianjin-Hebei region of China

To investigate the dynamical transmission behavior of pollutants and explore the roles played by monitoring stations in regional air pollutants transportation, we constructed a new model for the dynamical transmission index by adopting a statistics model that employs complex network analysis along with terrain data, meteorological variables, and air quality data. The study is conducted in Beijing-Tianjin-Hebei region with 70 stations in 13 cities. The findings indicated that the regional dynamical transmission networks were characterized by the participation of 67 out of 70 stations, as determined by node number. Among the model characteristics, the average path length and the average clustering coefficient, within the ranges of 2.08–2.32 and 0.26–0.51, respectively, maintained reasonable small-world characteristic. For the seasonal transmission features, the networks for PM2.5, PM10 in winter, and O3 in summer shared similar modeling characteristics with those of yearly networks. This suggested that the networks for these two seasons could represent the yearly transmission features. By employing the entropy weight method, the key monitoring stations numbered 1011 A, 1026 A, and 1010 A, which are located in Tianjin, Shijiazhuang, and Beijing, exerted significant impacts on air pollution transmission path in cities. The novel model has demonstrated its soundness and effectiveness in terms of capturing the behavior of transmission as well as the distinguishing roles of these crucial monitoring stations. This methodology could be employed for the construction of additional monitoring stations, identification of possible pollution sources, and prioritization of key pollution areas, thus providing valuable insights for environmental protection and management.