Local Pollution Research Articles

Day–night variation, size distribution, source identification, and health risk of polycyclic aromatic hydrocarbons in particulate matter in Ulsan, South Korea

Particulate matter (PM) in 11 different sizes was collected at a semi-rural site in Ulsan, a multi-industrial city in South Korea, over one year from 2019 to 2020 to investigate the temporal patterns in size distribution and potential sources of polycyclic aromatic hydrocarbons (PAHs). The mean concentration of Σ21 PAHs was higher in winter (0.29 and 0.45 ng/m3 for coarse and fine PM, respectively) than in summer (0.06 and 0.07 ng/m3). Although there was no statistically significant difference between day and night for the concentration of individual PAHs in both coarse and fine PM, the concentrations were generally higher during the day than at night. During the day, Σ21 PAHs had two distinct peaks (0.056–0.10 μm and 3.20–5.60 μm) in winter, but they tended to partition to larger particles (1.00–1.80 μm and >18.0 μm) in summer. This result suggests that the volatilization of PAHs in smaller particles was enhanced by high-temperature conditions in summer, followed by their condensation onto larger particles. At night, Σ21 PAH peaks were mainly around the size of 0.7 μm (droplet mode), suggesting the importance of heterogeneous-aqueous reactions. The results of the conditional bivariate probability function and concentration-weighted trajectory indicate that the high concentrations of PAHs in summer and winter were more affected by local pollution and long-range atmospheric transport, respectively. The results of the health risk assessment demonstrate that PAHs in fine PM contributed significantly to cancer risk compared to those in coarse PM. In conclusion, investigating the size distribution of PAHs in particles is necessary to understand their environmental behaviors and evaluate potential health risks.

Observation-Based Ozone Formation Rules by Gradient Boosting Decision Trees Model in Typical Chemical Industrial Parks

Ozone pollution in chemical industrial parks is severe and complicated and is significantly influenced by pollutant emissions and meteorological parameters. In this study, we innovatively investigated the formation rules of ozone by using observation-based analyses and a gradient-boosting decision tree (GBDT) model, focusing on a typical chemical industrial park located in the Yangtze River Delta of China. The results revealed that ozone concentration was positively correlated with temperature while negatively correlated with NO2 concentration and relative humidity (RH). Ozone pollution was predominantly observed from April to October (M4–10). The optimized GBDT model was subsequently utilized to establish a specific and quantifiable relationship between each single dominant impact factor (RH, NO2, temperature, and PM2.5) and ozone within a complex and uncertain multi-factor context during M4–10. Detailed discussions were conducted on the reaction rate of ozone-related to different levels of RH and temperature. The accumulation of ozone was favored by high temperature and low RH, with the maximum ozone concentration observed at the RH of 50% and the temperature of 35 °C. The NO2-O3 change curve exhibited distinct phases, including a period of stability, gradual increase, rapid increase, and equilibrium. During the second and third periods, the ratio of ozone production to NO2 consumption was 0.10 and 2.73, respectively. Furthermore, there was a non-monotonic relationship between variations in ozone concentration and PM2.5 concentration. Hence, it is imperative to implement fine control strategies in the park, such as adopting seasonal production strategies, implementing targeted measures for controlling NOx and active VOCs, and employing special control methods during periods of high temperature. This study provides aid in achieving effective management of localized ozone pollution and ensuring compliance with air quality standards.

Unveiling Microplastic Abundance and Distribution in an Oceanic Island: Offshore Depository or Local Pollution Indicator

Millions of tons of solid waste, mostly plastics, are annually dumped into the oceans, posing a major 21st-century environmental threat. Commercial shipping and ocean gyres exacerbate pollution on remote islands, resulting in the widespread presence of microplastics throughout the marine environment. Most of this pollution is plastics, and its fragmentation originates from microplastics (particles smaller than 5 mm). These debris are ubiquitous throughout the marine environment, dispersed among beaches, estuaries, on the water surface, and even on the seafloor. This study was carried out on São Miguel Island, Azores, where sediment samples were collected and analysed for microplastic (MP) abundance and dimension across eight areas covering the entire coastline of the island. Each area was divided into four sites along an inland–coastal gradient, from the stream to the ocean (upstream, downstream, coastal, and submerged marine sediments), following a nested design approach. In addition to this first method, 15 beaches, spread along the island’s shore, were also tested and compared. Fibres were the most common type of microplastic, with varying levels of abundance across different locations. Abundance generally increased closer to the coast, but there were occasional instances of high upstream concentrations due to heavy rains, which then accumulated near coastlines and beaches. This study revealed an important local MP source from an apparently pristine touristic region which, aligned with other recent findings, unveils an important and silent pollution issue potentially affecting oceanic islands that should be seriously addressed in future studies and raise concern for litter management and mitigation and environmental awareness actions.

Heavy Metal Pollution Status in The Main Surface Waters of The N'djili River Basin in Kinshasa, Democratic Republic of Congo: A Review with Application and Evaluation of Pollution Indices Evaluation

The aim of this study was to assess heavy metal pollution in the surface waters of the N'djili river watershed, as well as to determine the main sources of this pollution and their impact on water quality. Five sampling sites were selected in the watershed, representing a variety of urban and industrial influences. In these samples, trace metals such as lead (Pb), cadmium (Cd) and arsenic (As) were measured by atomic absorption spectrometry. The respective mean concentrations of lead (Pb), cadmium (Cd) and arsenic (As) in the surface waters of the N'djili basin are 0.066 mg/L, 0.006 mg/L and 0.310 mg/L. The mean value of the degree of contamination (Dc) in the surface waters of the N'djili basin is greater than 3, indicating heavy contamination of these waters (Lukaya river, Matete river, N'djili river, Ntshuenge river, Ngwele river). The average values of for the trace metal pollution index (HPI) are below the threshold value of 100, so overall, these waters are not very polluted. Nevertheless, particularly high concentrations were observed in the waters of the Matete River, with values exceeding 100, indicating severe localized pollution. Correlation analysis between trace metals and HPI indicates that lead and cadmium are the main contributors to surface water toxicity in the N'djili basin. These results highlight the urgent need to develop targeted strategies to reduce pollution in order to preserve public health and aquatic ecosystems.

AIR QUALITY MONITORING USING MACHINE LEARNING

Air pollution is a critical environmental issue with significant implications for public health and the well-being of ecosystems. This project focuses on developing an innovative solution for air pollution monitoring utilizing machine learning (ML) techniques. The primary objective is to design a system that can accurately predict, analyze, and monitor air quality in real time, providing valuable insights for effective pollution control and management. The proposed system incorporates a network of sensors strategically placed in various locations to capture diverse air quality parameters such as particulate matter, nitrogen dioxide, sulfur dioxide, and more. The collected data is then processed through ML algorithms to identify patterns, correlations, and trends, enabling the system to accurately predict air quality levels. The project aims to address traditional monitoring systems' limitations by leveraging ML models' adaptability and self-learning capabilities. By continuously updating and refining the models based on incoming data, the system becomes more adept at providing precise and timely information on air quality fluctuations. This endeavour contributes to a deeper understanding of local air pollution dynamics and empowers decision-makers with actionable insights for implementing targeted interventions. The integration of ML in air pollution monitoring represents a significant step towards creating sustainable and data-driven strategies for mitigating the adverse effects of pollution on human health and the environment. Index terms Air pollution, Machine Learning, Environmental Monitoring, Sustainability, Predictive Modeling, Sensor Network, Data Analysis.

Determinants of electric vehicle adoption: insights from Indian states

PurposeElectric Vehicles (EVs), owing to their low carbon emissions, have gained immense importance in achieving net-zero emissions by 2070. They have also appeared as viable substitute to conventional vehicles. Aligning with global initiatives, India has created a favourable ecosystem and has implemented several policies since 2011 to achieve its target. Consequently, the market share of EVs has surged, both globally and in India, over the past decade. Taking this into account, this study aims to identify the factors that influence EVs in a developing economy using the context of India.Design/methodology/approachThis study identified important determinants of EV adoption from global literature and employed a multiple linear regression model (MLRM) using the ordinary least squares (OLS) technique. Secondary data were utilised to identify determinants in the Indian context, sourced from the Ministry, NITI Aayog, AQI, the Lok Sabha Question, and the Economic Survey of India.FindingsThis study found that the number of charging stations and local pollution levels significantly influence EV adoption in India. The insignificance of the other variables may be due to the emerging state of the Indian EV market.Originality/valueThis study adds to the growing body of literature on EV adoption in developing economies by analysing the factors that impact its adoption using regional data. In addition, this study provides a unique perspective on a developing economy and advocates a comprehensive policy for EV adoption that reflects long-term sustainability.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-06-2023-0479.

Air pollution and stock returns: The cash flow risk channel

This paper provides a novel explanation regarding the phenomenon that high local air pollution forecasts a low risk premium at the firm-level. Using Chinese stock market data, we show that there is a negative correlation between air pollution and stock return. Further empirical analysis reveals that pollution affects the stock market by changing the covariance between cash flows and the aggregate profitability shock. This result can be illustrated by a neoclassical q-theory model with air pollution as a factor affecting production.

Impact of terephthalic acid emissions from intensive nocturnal biomass incineration on oxidative potential in Seoul, South Korea

This study investigated the impact of large-scale incineration facilities on PM2.5 levels in Seoul during winter. Due to the challenge of obtaining accurate combustion data from external sources, heat supply records were used as a proxy for combustion activity. To assess health risks, dithiothreitol-oxidative potential (DTT-OP) was analyzed to identify potential hazards to human health. By comparing DTT-OP with PM2.5 sources related to combustion, the study aimed to understand the impact of local pollution sources on human health in Seoul. The diurnal analysis showed that oxidative potential (0.19 μM/m3) and the biomass burning factor (5.53 μg/m3) peaked between 4:00 and 8:00 AM, with lower levels observed from 12:00 to 20:00. A significant correlation was found between combustion sources and oxidative potential, with a high correlation coefficient (r2 = 0.92). The presence of terephthalic acid (TPA) in the Cellulose combustion source profile, which is produced by the pyrolysis of plastics like polyester fiber and polyethylene terephthalate (PET), further supported the link to emissions from incineration facilities. These findings suggest that the biomass burning source is strongly correlated with DTT-OP, indicating a significant association with health risks among various local sources of PM2.5 in Seoul.

Evaluation of the effects of dust pollution on specific plant species near and around the marble mining site in Rajasthan, India.

Airborne particles (dust pollution) pose a significant threat to both human and plant populations. Plant leaves act as crucial biofilters, capturing significant amounts of air pollution; this characteristic offers a valuable tool to measure local pollution levels and assess individual plant species' ability to intercept and mitigate harmful dust particles. The present study was carried out to asses the effect of responses of various plant species to dust pollution near and around the marble mining site comprising residential site, highway area, and Central University of Rajasthan as control. The anticipated pollution index, air pollution tolerance index (APTI), dust absorption capacity, metal accumulation index (MAI), and biochemical factors were used to evaluate plant responses. Azadirachta indica A. Juss. demonstrated the highest (29.0) and Vachellia nilotica L. showed lowest (5.6) APTI, respectively. A. indica showed maximum MAI values in comparison to other plant species situated at residential site. Additionally, monitoring of particulate matter (PM10) observed to highest at highway, followed by mining, residential, and control sites. Overall A. indica representing highest APTI and effective dust capturing capacity at all sites could serve as potential pollution sinks. V. nilotica, with its very low APTI, can be marked as biomonitoring tool for detecting dust pollution.

Research on Light Pollution Evaluation Modeling Based on Comprehensive Weights

Light pollution refers to the excessive or improper use of artificial light. Global light pollution poses environmental and health problems. In order to measure and put forward reasonable suggestions to alleviate these phenomena, we established a light pollution evaluation model to calculate the risk degree of light pollution in a region, and sensitively put forward improvement suggestions to alleviate the degree of local light pollution according to the severity of local indicators. In order to strengthen the correlation between the indicators and the evaluation objectives in the evaluation model, we constructed the relationship between the degree of economic development and the night light data. Then, we analyzed the light pollution evaluation model from six perspectives: GDP, population density, forest area, terrain, temperature and precipitation. Combined with the data over the years, we made a preliminary prediction of the weights of each index, then carried out a consistency test on the weights of each index to judge their rationality, and finally conducted a sensitivity analysis.

The influence of long-range transported Saharan dust on the inflammatory potency of ambient PM2.5 and PM10

Although desert dust promotes morbidity and mortality, it is exempt from regulations. Its health effects have been related to its inflammatory properties, which can vary between source regions. It remains unclear which constituents cause this variability. Moreover, whether long-range transported desert dust potentiates the hazardousness of local particulate matter (PM) is still unresolved. We aimed to assess the influence of long-range transported desert dust on the inflammatory potency of PM2.5 and PM10 collected in Cape Verde and to examine associated constituents.During a reference period and two Saharan dust events, 63 PM2.5 and PM10 samples were collected at four sampling stations. The content of water-soluble ions, elements, and organic and elemental carbon was measured in all samples and endotoxins in PM10 samples. The PM-induced release of inflammatory cytokines from differentiated THP-1 macrophages was evaluated. The association of interleukin (IL)-1β release with PM composition was assessed using principal component (PC) regressions.PM2.5 from both dust events and PM10 from one event caused higher IL-1β release than PM from the reference period. PC regressions indicated an inverse relation of IL-1β release with sea spray ions in both size fractions and organic and elemental carbon in PM2.5. The PC with the higher regression coefficient suggested that iron and manganese may contribute to PM2.5-induced IL-1β release. Only during the reference period, endotoxin content strongly differed between sampling stations and correlated with inflammatory potency.Our results demonstrate that long-range transported desert dust amplifies the hazardousness of local air pollution and suggest that, in PM2.5, iron and manganese may be important. Our data indicate that endotoxins are contained in local and long-range transported PM10 but only explain the variability in inflammatory potency of local PM10. The increasing inflammatory potency of respirable and inhalable PM from desert dust events warrants regulatory measures and risk mitigation strategies.

Decoding the impact of fiscal decentralization on urban pollution intensity in China: A spatial econometric analysis

Utilizing city-level data from China, the paper employs a spatial econometric analysis to investigate the impact of fiscal decentralization on urban pollution. Empirical evidence indicates: (1) In the context of the emphasis of ecological civilization construction in China, an increase of fiscal autonomy for local governments is conducive to mitigating urban pollution intensity. Specifically, fiscal decentralization in one city not only promotes a reduction in local pollution intensity but alleviates environmental pollution problems in adjacent cities through spatial spillover effects. (2) Industrial structure upgrading and green technology progress become crucial measures for local governments to realize pollution reduction targets through fiscal expenditure. (3) Heterogeneity analysis reveals that the positive significance of decentralization is most prominent in the eastern China, while local governments with fiscal autonomy in central region tend to transfer pollution to neighboring cities. (4) There is a threshold characteristic for fiscal decentralization to promote a reduction in urban pollution intensity, and its marginal effect becomes more significant accompanied by continuous introduction of sophisticated foreign direct investment. Finally, the paper summarizes the potential significance of fiscal decentralization among Chinese local governments against the background of “Chinese-style decentralization” and proposes corresponding policy recommendations.

A Situation and Temporal Behaviors of Air Pollution by ARIMA Model: A Case Study of Chiang Mai

The persistent issue of haze has become a paramount concern in various regions, impacting air quality, human health, and overall environmental well-being. This study focuses on the case of Chiang Mai, a city prone to recurrent haze times. This research reviews haze situation and its temporal behaviors to propose successive opportunities for effectively addressing the haze problem in Chiang Mai, encompassing both short-term mitigation measures and long-term sustainable strategies by applying haze data from 1996 to 2022 derived from Yupparaj school ground-based station recorded by Pollution Control Department (PCD) for analyzing the ARIMA statistics. According to the study's findings, the daily averages of PM10 and PM2.5 move greatly from January to April. There is a minor change in the daily average carbon monoxide (CO) between January and April. Although short-term projections in light of the area's pollution levels, it may be indicated that PM10, PM2.5, SO2, NO2, and O3 will likely increase, regarding the local pollution's changing behavior. When compared to other working days and weekends, it was discovered that the average CO values were significantly different at the 0.01 level, with Friday having the highest average CO emissions. Consideringly the timing, it was discovered that the CO2 levels were at their highest during rush hour, peaking in the evening and then the morning and noon periods, respectively. These characteristics indicate that measures taken to address this problem should concentrate on operating at a time that is relevant to the time the air pollution arises as well as the influences reducing and mitigating the level of air pollution greatest efficiency.

Chemical composition and origins of PM2.5 in Chiang Mai (Thailand) by integrated source apportionment and potential source areas

Upper Southeast Asian region lacks source apportionment studies of air pollution, particularly PM2.5, despite its persistence. This study aims to quantify PM2.5 source contributions and identify their potential source areas in urban Chiang Mai (Thailand) as a representative of the region, using the positive matrix factorization (PMF) receptor model and the potential source contribution function (PSCF). A total of 51 samples were collected and analyzed for water-soluble ions, organic carbon, elemental carbon, carbohydrates, carboxylates, and metals. An average PM2.5 concentration of 116 ± 35 and 35 ± 6 μg m−3 was found during smoke-haze (SH) and non-smoke-haze (NSH) periods, respectively. The average PMF-resolved contributions to PM2.5 in the SH period were the following: biomass burning (59.3 μg m−3) > secondary sulfate (26.2 μg m−3) > dust (16.1 μg m−3) > traffic (14.6 μg m−3). In contrast, those in the NSH period were the following: traffic (27.2 μg m−3) > dust (5.9 μg m-3) > biomass burning (2.5 μg m−3). The PSCF revealed that biomass burning has the main potential source area along the Thai-Myanmar border, while the secondary sulfate largely originated in Indian continent. Local pollution from traffic and dust contributed consistently but was minor in comparison. The source composition exhibited major seasonal change because of the lack of biomass burning and transboundary pollution in the wet season, where exhaust and non-exhaust traffic emissions became the main contributors. These findings offer insights into how regional PM2.5 affects the local pollution in the urban atmosphere.

Heavy Metal Pollution Status and Potential Ecological Risk Assessment in Farmland Surface Soil Along River in Jiaozuo Area

A total of 41 surface soil samples were collected from the farmlands along Guangliji River and Jianggou River in Jiaozuo area, and 7 heavy metals such As Cr, Cu, Zn, AS, Cd, Pb and Ni were determined by ICP-MS (Varian-820).The soil accumulation index method, single factor pollution index method, Nemerow comprehensive pollution index method and potential ecological risk index method were used to evaluate the pollution status and potential ecological risk of heavy metals in the study area. The results showed that compared with the risk screening value (pH>7.5) given in the "Soil Quality Standard for Soil Pollution Risk Control of Agricultural Land" (GB15618-2018), Cd in farmland soil along the two rivers exceeded the screening value by 5.7 times and 1.5 times, respectively, and was the main local pollutant. The Nemero comprehensive pollution index of the farmland along Guangliji River and Jianggou River was 9.71 and 1.88, respectively, which were in the level of heavy pollution and light pollution. The comprehensive potential ecological risk index of farmland along the two rivers is 417.65 and 85.79, with strong ecological risk and slight ecological risk respectively. The comprehensive evaluation shows that there is a certain degree of heavy metal pollution in the farmland soil along the river in Jiaozuo area, especially Cd pollution, and the ecological risk in some areas has exceeded the acceptable level, which should arouse the attention of the local government and relevant departments.

Egoistic value is positively associated with pro-environmental attitude and behaviour when the environmental problems are psychologically close.

Egoistic value is conceptualized as anti-environmental in many environmental value theories, yet contradictory evidence exists for its relation with pro-environmental attitude and behaviour. To provide insights into these inconsistent findings, this research examined the moderating role of the psychological distance of environmental problems on their relationship. Across one cross-sectional survey study (1008 community participants from the United States) and one World Values Survey study (66,704 nationally representative participants from 46 countries/regions), results converged in showing that psychological distance of environmental problems (i.e. climate change and local pollution) moderated the relationship between egoistic value and pro-environmental attitude and behaviour. Their association became more positive as that psychological distance got closer. Different patterns were observed for altruistic and biospheric values. These findings highlight the potential pro-environmental utility of egoistic value and the importance of paying attention to contexts when theorizing its relation with pro-environmental attitude and behaviour.

Uncovering potential mangrove microbial bioindicators to assess urban and agricultural pressures on Martinique island in the eastern Caribbean Sea

Martinique's mangroves, which cover 1.85 ha of the island (<0.1 % of the total area), are considerably vulnerable to local urban, agricultural, and industrial pollutants. Unlike for temperate ecosystems, there are limited indicators that can be used to assess the anthropogenic pressures on mangroves. This study investigated four stations on Martinique Island, with each being subject to varying anthropogenic pressures. An analysis of mangrove sediment cores approximately 18 cm in depth revealed two primary types of pressures on Martinique mangroves: (i) an enrichment in organic matter in the two stations within the highly urbanized bay of Fort-de-France and (ii) agricultural pressure observed in the four studied mangrove stations. This pressure was characterized by contamination, exceeding the regulatory thresholds, with dieldrin, total DDT, and metals (As, Cu and Ni) found in phytosanitary products. The mangroves of Martinique are subjected to varying degrees of anthropogenic pressure, but all are subjected to contamination by organochlorine pesticides. Mangroves within the bay of Fort-de-France experience notably higher pressures compared to those in the island's northern and southern regions. In these contexts, the microbial communities exhibited distinct responses. The microbial biomass and the abundance of bacteria and archaea were higher in the two less-impacted stations, while in the mangrove of Fort-de-France, various phyla typically associated with polluted environments were more prevalent. These differences in the microbiota composition led to the identification of 65 taxa, including Acanthopleuribacteraceae, Spirochaetaceae, and Pirellulaceae, that could potentially serve as indicators of an anthropogenic influence on the mangrove sediments of Martinique Island.

Marine fuel restrictions and air pollution: A study on Chinese ports considering transboundary spillovers

Docked vessels in ports can be one of the dirtiest emitters in terms of local air pollutants. This paper evaluates whether the regulation of Emission Control Areas in China reduces air pollutants emissions in port cities. The transboundary spillovers of air pollution, induced by meteorological conditions, results in air pollution concentrations at specific locations encompassing both locally generated pollutants and those originating from elsewhere. Therefore, it is challenging to discern the emission reduction effects of environmental regulatory policies through air pollutant monitoring concentrations. This paper develops an approach to isolate transboundary spillovers induced by the wind, estimate locally produced pollution, and utilize the concentrations of locally produced pollution to identify the treatment effects of the Chinese Emission Control Areas policy. The results find that the policy has significantly reduced the locally produced sulfur dioxide concentrations by 3.41% in port cities. However, the omission of transboundary spillovers could yield contrasting results, suggesting that ignoring the pollution transport could lead to erroneous conclusions. The research findings of this study hold significant policy implications, highlighting the importance of accounting for transboundary transport of air pollution in evaluating the local government’s environmental efforts and implementing supra-city environmental policies to prevent air pollution.

Neutrality and impartiality in Midwestern U.S. newspapers: community-oriented newspaper journalists reporting of environmental water problems in agricultural and ranching states

ABSTRACT Background U.S. journalists embedded in rural and agricultural communities could adversely affect the health of residents if they avoid alerting and engaging their readers – farmers, ranchers, and community members – on environmental and health issues. We expected reporters would maintain community status quo and inaction by framing local water pollution and quality issues neutrally deemphasizing threats and solutions to maintain their own credibility as unbiased informational sources. Method In a content analysis of local water quality newspaper articles from five farming and cattle ranching states in the west central U.S. Midwest, we employed seven variables to investigate whether journalists practiced neutral, detached forms of journalism (i.e. dissemination versus interpretative role enactment, government-frame) as well as whether they deemphasized water pollution as a concerning issue (i.e. problem, threat), water pollution solutions, and readers’ efficaciousness. Results The results showed these journalists relied heavily on government-driven narratives presenting water quality issues from an impartial, straight reporting lens in which they primarily followed the journalistic dissemination role enactment, while neglecting to provide readers with interpretative, threat, efficacy, or solution’s information. Conclusions The study seeks to help communicators understand the information diet people living in this part of the country likely receive on environmental and health risks in the context of water pollution. Communicators seeking to reach and affect audiences in this region should understand local information practices to navigate how to craft culturally specific public health messages.

The impact of pollution transmission networks in a transboundary pollution game

To explore transboundary pollution games with network externalities, this study develops a multi-region differential game which investigates the interaction and co-evolution of multiple pollution stocks across a continuous horizon. Pollution transmission networks are incorporated into the model via an adjacency matrix, detailing transmission paths of pollution across regions. Equilibrium strategies for emissions and investments in pollution control are obtained through a system of coupled differential equations. Our study demonstrates that these equilibrium strategies and the corresponding discounted net welfare of regions depend crucially on the structure of pollution transmission networks. The spectral properties of networks can effectively discern the evolutionary stability of equilibrium strategies. Through a numerical simulation of multiregional pollution dynamics, we also show that regions' motivation for pollution control decreases continuously as their influence in pollution transmission networks (i.e., network influence) increases; Nevertheless, an increase in a region's network influence can significantly reduce local pollution stocks in cycle networks but increase them in tree networks.