Air Pollution Issues Research Articles

Review of IoT Systems for Air Quality Measurements Based on LTE/4G and LoRa Communications

The issue of air pollution has recently come to light due to rapid urbanization and population growth globally. Due to its impact on human health, such as causing lung and heart diseases, air quality monitoring is one of the main concerns. Improved air pollution forecasting techniques and systems are needed to minimize the human health impact. Systems that fall under the Internet of Things (IoT) topology have been developed to assess and track numerous air quality metrics. This paper presents a review of IoT systems for air quality measurements, where the emphasis is placed on systems with LTE/4G and LoRa communication capabilities. Firstly, an overview of the IoT monitoring system is provided with recent technologies in the market. A critical review is provided of IoT systems regarding air quality using LTE/4G and LoRa communications systems. Lastly, this paper presents a market analysis of commercial IoT devices in terms of the costs, availability of the device, particulate matter each device can measure, etc. A comparative study of these devices is also presented on LTE/4G and possibly LoRa communications systems.

Innovative SVM optimization with differential gravitational fireworks for superior air pollution classification

Amid escalating tension between environmental conservation and economic development, the imperative to enhance air quality has become increasingly urgent. This study elucidates a sophisticated approach for the assessment and remediation of air pollution issues through the integration of an enhanced particle swarm optimization algorithm and a differential gravitational fireworks algorithm-optimized support vector machine (SVM). In the initial phase of this research, a series of intricate data preprocessing and augmentation procedures were conducted, and the differential evolution algorithm played a pivotal role. The differential gravitational fireworks algorithm was subsequently introduced to optimize the SVM parameter settings, thereby bolstering classification accuracy and mitigating issues such as overfitting. Through rigorous and meticulous empirical testing, the augmented SVM model demonstrated notable performance in terms of classification accuracy and sequential and nonsequential data fusion, surpassing conventional SVM techniques. Notably, our sequential fusion method achieved an accuracy of up to 91%, at least 3% higher than that of nonsequential techniques. In conclusion, this study reveals an innovative and enhanced technological approach that is highly effective for the precise measurement and control of air pollution levels.

Urban Green Infrastructures and Its Impacts on the Urban Environment: A Review

Urban green infrastructure plays a crucial role in preserving a green urban environment, thus contributing significantly to human health. The absence of such infrastructure can lead to environmental challenges, particularly in the face of escalating global urbanization. Green urban infrastructure proves essential for maintaining cleanliness in urban areas, especially as major cities worldwide grapple with escalating air pollution exacerbated by the inadequate presence of green spaces, green roofs, green walls, and domestic and private gardens emerge as noteworthy contributors to mitigating these challenges. As the global population steadily increases, a collective effort is imperative to enhance green infrastructure and cultivate public awareness to address the pressing issue of air pollution in cities. Green spaces serve as a pivotal indicator of urban verdancy, offering a myriad of benefits, including environmental cleanliness, the creation of recreational spaces, the promotion of health and well-being, the facilitation of children’s mental development, the presentation of aesthetically pleasing urban landscapes, temperature reduction, and pollution mitigation. This comprehensive review analyzed 60-70 studies on urban green infrastructures and their impacts on the urban environment. A library research methodology was used, focusing on literature from Google Scholar published in the last two decades which delves into the multifaceted aspects of urban environmental cleanliness, providing valuable insights for policymakers, and urban planners with a profound understanding of their impacts. Stakeholders can proactively implement measures to enhance cleanliness in the urban environment.

Forecasting Daily Air Quality Index and Early Warning System for Estimating Ambient Air Pollution on Road Networks Using Gaussian Dispersion Model with Deep Learning Algorithm

The rapid growth of the vehicle population is a major factor in heavy air pollution and public health issues. Traffic-related air pollutants (TRAPs) on roads are often much higher than ambient values, leading to high exposure levels in vehicles. This research proposes a hybrid forecasting model for early detection and early warning systems (EWS) of road networks during real-world travels. Data is collected from Kannur, Calicut, Palakkad, and Coimbatore using real-time sensors, including surrounding discussion information, activity information, vehicle speed, and stopping events. The study predicts ambient air quality (AAQ) levels on the road network using the Gaussian Dispersion model (GDM) and measures the risk sensitivity of PM10 and PM2.5 in selected regions. This helps formulate powerful prevention strategies and prevent negative health impacts. The air pollution module for predicting concentration has an innovative hybridization model that combines an improved cuckoo search (CS) and differential evolution (DE) algorithm with a stacked LSTM model to increase forecasting accuracy of six major environmental pollution levels. This model predicts the AAQ level and is effective and robust for warning one day before the pollutant event occurs based on the risk level of an ambient air pollutant from the RN.

Air Pollution and Urban Economic Growth: A Study Based on Dynamic Models

<p class="Paragraph"><span lang="EN-US">With China's economic growth, the issue of air pollution has become increasingly prominent. As the core of political and economic development, 184 major cities in China face severe air pollution and significant environmental pressures due to high population density. Consequently, China has established key urban environmental monitoring stations in these cities to monitor pollution emissions in real time. This paper utilizes panel data from 184 Chinese cities spanning 2012-2022 and employs dynamic GMM models, fixed effects models, and mediation effect models to empirically examine the impact of air pollution on urban environmental pollution and its mediation mechanisms. From the perspective of cities with different income levels, a dynamic GMM model is established to determine the correlation between air pollution and various factors influencing urban economic growth. The study finds that air pollution significantly reduces economic growth in Chinese cities at the 1 per cent level, with each unit increase in air pollution reducing economic growth in Chinese cities by 0.84 units. Urbanization and human capital are identified as two crucial channels through which air pollution affects the economic development of Chinese cities. The negative impact of air pollution on economic growth is more pronounced in larger cities compared to smaller ones, and the adverse effects of haze pollution become increasingly significant over time. Therefore, policymakers should take the cultivation and application of new quality productivity as a key strategy to enhance the quality of economic development, and realise a win-win situation between economic growth and environmental protection through scientific and technological innovation and industrial restructuring. This will not only help alleviate the air pollution problem, but also inject new momentum into the high-quality development of China’s economy.</span></p>

Clarifying the impact of engine operating parameters of heavy-duty diesel vehicles on NOx and CO2 emissions using multimodal fusion methods

The issue of air pollution from transportation sources remains a major concern, particularly the emissions from heavy-duty diesel vehicles, which pose serious threats to ecosystems and human health. China VI emission standards mandate On-Board Diagnostics (OBD) systems in heavy-duty diesel vehicles for real-time data transmission, yet the current data quality, especially concerning crucial parameters like NOx output, remains inadequate for effective regulation. To address this, a novel approach integrating Multimodal Feature Fusion with Particle Swarm Optimization (OBD-PSOMFF) is proposed. This network employs Long Short-Term Memory (LSTM) networks to extract features from OBD indicators, capturing temporal dependencies. PSO optimizes feature weights, enhancing prediction accuracy. Testing on 23 heavy-duty vehicles demonstrates significant improvements in predicting NOx and CO2 mass emission rates, with mean squared errors reduced by 65.205 % and 70.936 % respectively compared to basic LSTM models. This innovative multimodal fusion method offers a robust framework for emission prediction, crucial for effective vehicle emission regulation and environmental preservation.

Monitoring and Predicting Air Quality with IoT Devices

The growing concern about air quality and its influence on human health has prompted the development of sophisticated monitoring and forecast systems. This article gives a thorough investigation into forecasting the air quality index (AQI) with an Internet of Things (IoT) device that analyzes temperature, humidity, PM10, and PM2.5 levels. The dataset used for this analysis comprises 5869 data points across six critical parameters essential for accurate air quality prediction. The data from these sensors is sent to the ThingSpeak cloud platform for storage and preliminary analysis. The system forecasts AQI using a TensorFlow-based regression model, delivering real-time insights. The combination of IoT technology and machine learning improves the accuracy and responsiveness of air quality monitoring systems, making it a useful tool for environmental management and public health protection. This work presents comparatively the effectiveness of feedforward neural network models trained with the ‘adam’ and ‘RMSprop’ optimizers over different epochs, as well as the machine learning algorithm random forest with varying numbers of estimators to forecast AQI. The models were trained using both types of regression analysis: linear regression and random forest regression. The findings show that the model achieves a high degree of accuracy, with the predictions closely aligning with the actual AQI values, thus having the potential to significantly reduce the negative health impact associated with poor air quality, protecting public health and alerting users when pollution levels are higher than allowed. Specifically, the random forest model with 100 estimators delivers the best overall performance for both AQI 10 and AQI 2.5, achieving the lowest Mean Absolute Error (MAE) of 0.2785 for AQI 10 and 0.2483 for AQI 2.5. This integration of IoT technology and advanced predictive analysis addresses the significant worldwide issue of air pollution by identifying the pollution hotspots and allowing decision-makers for quick reactions, and the development of effective strategies to reduce pollution sources.

Analysis of Public Satisfaction Levels in Gelora Bung Karno (GBK) City Forest Based on Socio-Cultural Functions

The Jakarta City Government, along with the central government, has implemented various measures to address the issue of air pollution that has become a serious problem for the city. One of the efforts undertaken is to increase the provision of urban green spaces and restore the function of the GBK City Forest as a public green open space (GOS). However, there has been no research showing the public's perspective and satisfaction with the utilization of the GBK City Forest. This study aims to determine the level of public satisfaction with the GBK City Forest based on social-cultural functions. The assessment indicators used to measure public satisfaction in this study are based on the theory of cultural ecosystem services developed by Shijie Gai et al. The data used in this study were obtained using mixed methods data collection techniques through questionnaires (quantitative) as well as in-depth interviews and literature studies (qualitative). Based on the results of the study, it can be concluded that the majority of the public is highly satisfied with the utilization of the social-cultural functions of the GBK City Forest. However, some people still complain about the lack of supporting facilities for recreational activities and the disturbances from external parties that make them feel uncomfortable when engaging in activities at the GBK City Forest. Therefore, it is important for the government and the GBK City Forest management to consider public feedback and improve the quality and safety of the existing facilities.

인천항 대기오염물질 배출량 산정 및 저감효과 분석

As global awareness of air pollution issues caused by ports increases, international organizations and developed countries are monitoring pollution levels and establishing various eco-friendly policies to reduce emissions. Domestically, the relevant laws were enacted in 2020 to systematically manage the sources of port pollutants in the five major ports. This study aims to estimate the emissions of air pollutants generated by Incheon Port and analyze the effects of major eco-friendly policies such as Emission Control Areas (ECA), Vehicle Speed Reduction (VSR), and Alternative Maritime Power (AMP). For the year 2023, activity data for all ships, cargo handling equipment, and vehicles were collected, applying methodologies from reputable institutions such as the European Environment Agency (EEA) and the Environmental Protection Agency (EPA). According to national guidelines, air pollutants are categorized into eight types: CO, NOx, SOx, TSP, PM10, PM2.5, VOC, and NH3. The analysis revealed that the total emissions of air pollutants from Incheon Port were approximately 6,362 tons, with ships accounting for over 60% of the total emissions. Among the pollutants, NOx was the highest at 4,597.4 tons, followed by CO at 862.4 tons, and TSP at 314.6 tons. Among the ports, emissions were highest at the New Port, followed by the South Port, Inner Port, and North Port. The results of this study provide essential baseline data for monitoring the air pollution levels of Incheon Port and serve as critical evidence for environmental management and policy development. For sustainable future management, it is crucial to secure reliable data and develop a platform that links data collection, analysis, and visualization functions for more systematic operation.

Assessing and anticipating environmental challenges in Lahore, Pakistan: future implications of air pollution on sustainable development and environmental governance.

Urban environment and air quality are changing primarily due to land use land cover (LULC) changes, economic activity, and urbanization. Air pollution has been increasingly acknowledged as a major issue for cities due to its extensive effects on health and well-being. As the second most populous city in the country, Lahorefaces alarming levels of air pollutants, whichinduced this study to focus on the pervasive issue of air pollution in Lahore. For this, the study collected air pollutants data from the Environmental Protection Department of Punjab and analyzed them using the ARIMA model. In the research results, both the observed data and predictive models uncovered concerning trends in pollutant concentrations, ultimatelyportraying a concerning picture for air quality management. Carbon monoxide (CO) levels show a consistent rise, surpassing Pakistan's environmental standards by 2025. Similarly, nitrogen dioxide (NO2) concentrations escalate, exceeding prescribed standards. Ground-level ozone (O3) also demonstrates a substantial increase, surpassing standards by 2025. Both PM2.5 and PM10 exhibit marked upward trends, projected to exceed recommended limits, particularly PM10 throughout the study year. The Air Quality Index exhibits an observable upward trend, fluctuating between 70 and 442 from 2015 to 2020. Similarly, a positive correlation was found between population growth and land use conversion into residential areas. Projections suggest a continuous increase, potentially hitting a severe level of 500 during winter by 2025. These findings point to an impending air pollution crisis, demanding urgent action to address the hazardous situation in the city. The study recommends that urban air pollution should be reduced, and the negative health effects of air pollution should be minimized using vegetation barriers, screens, and greening initiatives. Strict regulations and monitoring initiatives need to be put in place in big cities to monitor pollution and vegetation.

Empowering London Primary School Communities to Know and Tackle Air Pollution Exposure

This study tested the effectiveness of participatory methods to engage primary school communities in London with air pollution issues to help them identify, understand, and reduce their air pollution exposure. Three primary schools with high pollution levels participated in environmental educational activities, including air quality workshops and field campaigns involving different methods, time schedules, and project scopes. Participants completed surveys before and after these activities to measure their understanding, perceptions, and behavior. After the workshops, students admitted feeling more skillful in monitoring air quality and confident in incorporating this education into daily life and teaching others. They shared their results with peers, school leadership, and the community and led campaigns to help design experiments, choose measurement locations, and reduce air pollution exposure in schools. The results showed increased concern about air pollution, a better understanding of its causes and impacts, and more reported reduction behaviors. An Air Pollution Exposure Index (APEI) was combined with participatory monitoring data to raise awareness and understanding among students on typical school-day exposure. Feedback indicates that the tool was useful in defining air pollution exposure status and identifying exposure risks. Overall, this study contributes to the evidence of the feasibility and impact of student-led environmental education interventions These interventions involve collecting personalized exposure data, co-planning mitigations with school community groups, and employing methods to raise awareness of air pollution and empower pupils to identify potential solutions and change behaviors.

Advancements in miniaturized infrared spectroscopic-based volatile organic compound sensors: A systematic review

The global trends of urbanization and industrialization have given rise to critical environmental and air pollution issues that often receive insufficient attention. Among the myriad pollution sources, volatile organic compounds (VOCs) stand out as a primary cluster, posing a significant threat to human society. Addressing VOCs emissions requires an effective mitigation action plan, placing technological development, especially in detection, at the forefront. Photonic sensing technologies rooted in the infrared (IR) light and matter interaction mechanism offer nondestructive, fast-response, sensitive, and selective chemical measurements, making them a promising solution for VOC detection. Recent strides in nanofabrication processes have facilitated the development of miniaturized photonic devices and thus sparked growing interest in the creation of low-cost, highly selective, sensitive, and fast-response IR optical sensors for VOC detection. This review work thus serves a timely need to provide the community a comprehensive understanding of the state of the art in this field and illuminate the path forward in addressing the pressing issue of VOC pollution.

Nanotechnology at the Crossroads: Advancements and Ethical Dilemmas in Healthcare, Environment and Agriculture

With the advancements in nanotechnology, the interaction between nanotechnology, society and environment has increased. Nanotherapeutics and nanopharmaceuticals have allowed facilitation of earlier and more precise diagnosis, reduced side effects, improved targeted therapies and efficacy of drugs. Likewise, nanotechnology has helped in improving the quality of environment by solving issues of air pollution, water remediation and waste management with the help of nanoproducts such as nanofilters, nanophoto catalysts, nanoadsorbents and nanosensors. Moreover, nanopesticides, nanofood, anti-bacterial nanopackaging, nanofertilizers and many other products have helped food and agriculture sector to grow. There are innumerable products on shelf based on nanotechnology impacting almost every sector. However, nanotechnology like any other technology if used unchecked and unregulated can be a cause of social, environmental, legal and ethical concerns. Therefore, it is crucial to consider these challenges in addition to the promises and opportunities nanotechnology has to offer. This review has highlighted the immense importance of nanotechnology by discussing its applications especially in medicine, environmental sciences and food and agriculture sectors. Closely studying these aspects will allow us to discover gaps, obstacles and potential solutions for responsible nanotechnology development and deployment. Understanding these concerns and challenges is also critical for policymakers, researchers, industrialists and society as a whole in order to promote ethical practices and informed decision-making. This review will help to contribute to the continuing discourse and raise ethical awareness in the field of nanotechnology thence minimizing the harm while maximizing the benefits.

Unveiling pollutants in Sonipat district, Haryana: Exploring seasonal, spatial and meteorological patterns

Air pollution is a serious environmental problem that aggravates climate change. Sonipat district, is a fast-urbanizing area with serious air pollution issues brought on by a variety of industry, busy traffic, and agricultural methods. Since in-situ measurements of air pollution are not feasible everywhere on Earth's surface, satellite data sets are crucial for determining trends in air pollution. This study uses several data sets, such as Sentinel-5P air pollution data, CHIRPS rainfall data, and GPWv4 population density data to investigate the spatial and temporal fluctuations of air pollution with reference to meteorological and other important socioeconomic factors. Pollutants include Carbon Monoxide (CO), Nitrogen Dioxide (NO2), Sulphur Dioxide (SO2), Ozone (O3), and Absorbing Aerosol Index (AAI) are the main focus of the analysis during the winter, summer, and post-monsoon seasons. Findings revealed that the district's southeast portion has greater concentrations of CO and NO2, which are likely due to the intense traffic and industrial activity along National Highway 44. While O3 and SO2 exhibit seasonal fluctuations correlated with temperature and sunlight, AAI levels peak in the October in post-monsoon season as a result of regional wind patterns and agricultural burning. Rainfall during the September in post-monsoon season lowers the concentrations of pollutants because of retreating monsoon. Pollutant dispersion is greatly impacted by wind direction and speed. The study highlights the significance of weather-related elements in influencing air pollution levels and emphasizes the necessity for efficient air quality management and pollution mitigation techniques.

Study on Mapping and Identifying Risk Areas for Multiple Particulate Matter Pollution at the Block Scale Based on Local Climate Zones

As China’s urbanization process accelerates, the issue of air pollution becomes increasingly prominent and urgently requires improvement, based on the fact that environmental conditions such as meteorology and topography are difficult to change. Therefore, relevant optimization studies from the perspective of architectural patterns are operable to mitigate pollution. This paper takes the Wenhua Road block in Shenyang, China, as the research object; obtains the concentration data of three kinds of particulate matter through fixed and mobile monitoring; and analyzes the spatial distribution characteristics of Local Climate Zones ( LCZ) and particulate matter in the block based on the ArcGIS platform, identifies high-risk areas, and excavates the influence of LCZ on the concentrations of three kinds of particulate matter. The results show that the spatial distribution characteristics of PM1, PM2.5, and PM10 under the same pollution level are relatively similar, while the spatial heterogeneity of the distribution of the same particulate matter under different pollution levels is higher. The time-weighted results show that the PM1 pollution level in the block ranges from 44 to 51 μg/m³, PM2.5 ranges from 75 to 86 μg/m³, and PM10 ranges from 87 to 99 μg/m³. The pollution hot spots throughout the year are located in the central, eastern and western parts of the study area. In terms of the relationship between the LCZ and particulate matter, with the increase in the particulate matter diameter, the correlation between the three kinds of particulate matter and LCZ are all enhanced. The built-up LCZ always has a larger average concentration of particulate matter than that of the natural LCZ, and building height and building density are the main factors causing the difference. In the optimal design of the risk area, the proportion of natural vegetation or water surface should be increased and the building height should be properly controlled and the building density should be reduced in the renewal of the urban building form. This study will largely improve the spatial refinement of the optimization of urban architectural patterns oriented to mitigate particulate matter pollution.

Air pollution tolerance index of selected tree species in urbanized areas of Butuan City, Philippines

Assessing the sensitivity or tolerance of plants to air pollution is a valuable method to address the issue of air pollution in a locality. The aim of this study was to examine the Air Pollution Tolerance Index (APTI) of selected tree species in significant locations in Butuan City. This study purposively sampled 19 tree species collected from three areas in Butuan City. The trees were evaluated using APTI which consists of four parameters: leaf-extract pH, relative water content, total chlorophyll content, and ascorbic acid content. The selected tree species were categorized as tolerant, intermediately tolerant, and sensitive based on the calculated APTI. The APTI results revealed that only three species namely Ficus elastica, Premna odorata, and Mangifera altissima were identified to be tolerant species and could be suitable to act as sinks of air pollutants. Sensitive species were Inocarpus fagifer and Terminalia catappa. APTI can be a useful tool that can help plan and decided which tree species to plant in greenbelt areas in the city to remediate air pollutants.

Identifying critical assumptions and risks in air quality management planning using Theory of Change approach

Governments across the world have been developing legislation and policy implementation instruments to address air pollution issues. Air Quality Management Plans (AQMPs) have emerged as a key instrument, with most developed countries having established effective AQMPs. These take a variety of forms depending on the overarching national framework for environmental regulations. The uptake of AQMPs in low and middle-income countries, however, has been less broad and, even in those that have adopted AQMPs, their effectiveness and efficiency have been limited, especially in urban areas. South Africa has adopted AQMPs and is recognised as having one of the most mature and complex air quality management legal frameworks in the world. Yet South Africa is still facing serious air quality challenges, especially in those areas that have been declared as priority areas. This paper, therefore, aims to identify critical assumptions and risks as a basis to evaluate the effectiveness of AQMPs as a policy instrument through the application of Theory of Change (ToC) approach. This study has resulted in the development of the ToC map, identification of 15 critical assumptions and associated causal narrative framework. In addition, this paper identified key risks underpinning the AQMP development and implementation process in South Africa. The identified critical assumptions embedded within different components of the ToC framework are not the only assumptions relating to this policy instrument, however, they are the fundamentally important ones that may significantly impact the success or failure of the AQMP system in South Africa if not managed. This study suggests that in order to further understand the challenges relating to the effective development and implementation of AQMPs, it is important that further research be conducted to test the validity of these critical assumptions which will provide solutions towards avoiding or mitigating risks associated with them.

An Extended Theory of Planned Behaviour for Explaining Chinese Domestic Electric Vehicle Purchasing Behaviour

Compared to traditional fuel-powered cars, electric vehicles (EVs) are widely acknowledged as a viable form of transportation and an effective way to address the issues of energy scarcity and air pollution. However, the majority of countries, including China, have recently lowered their preferential treatment and will instead adopt a market-driven strategy to promote EVs. Thus, it is critical to identify the particular elements that support and undermine consumer decisions to choose EVs. This study investigates the impact of perceived value (PV) and consumer ethnocentrism (CE), along with components of TPB, on consumers’ intentions towards the adoption of domestic EVs by using a convenience sample method to collect data. The results showed that attitude, subjective norm (SN), and perceived behavioural control (PBC) positively influence intention. SN positively influences attitude, and attitude plays a mediational role between SN and intention. In addition, PV and CE positively influence attitude towards purchasing domestic EVs. Last, the theoretical and practical implications and limitations are discussed accordingly.

Linking energy efficiency and waste recovery to improve housing insulation and tackle energy poverty in south-central Chile

Given the environmental impact of the construction industry, sustainable solutions are increasingly needed to minimize the life cycle impacts of buildings by means of reusing renewable industrial waste. Abundant forest and farm waste exist in center-south Chile, with great potential to be used as thermal insulation and, thus, improve the livability of houses located in an area facing air pollution and energy poverty issues. This paper discusses two types of insulation produced and tested jointly with Germany-based Fraunhofer Institute for Wood Research (WKI)– using Chilean agroforestry waste: First, a flakeboard made of wood veneer chips; and second, a flexible substrate and a pressure resistant board made of wheat and oat straw. The main goal of this research is to assess, by means of digital modeling and experimental tests, the thermal characteristics and the resistance to humidity of these new insulation materials and their performance within construction solutions usually used in Chile. The benchmarks used were the most demanding national residential energy efficiency standard implemented to address air pollution and energy poverty, and the most demanding international standard, both for walls and roofing. The results of both insulation materials was promising, as they met the thermal performance and resistance to humidity requirements and show technical feasibility for the insulation market; in addition, they share the potential to be used in different types of buildings and construction solutions both in Chile and abroad, promoting a sustainable approach starting from the raw materials and the manufacturing process, through the buildings’ operations.

Is air pollution the original sin of firms? The impact of air pollution on firms' ESG scores

This is the first article to demonstrate the negative impact of air pollution as an informal institution on firms' ESG scores with listed manufacturing firms in China from 2010 to 2020. It is based on causal attribution theory and risk perception theory. We argue that, under the current ESG rating system, air pollution issues may be overly attributed to firms, resulting in decreased ESG scores. We find that when PM2.5 pollution concentration increases by 1 μg/m3, firms' ESG scores decrease by roughly 0.3 points. These results remained significant after a series of robustness tests and instrumental variable regressions. We find that firms in heavily polluting industries are almost twice as affected as those in non-heavily polluting industries. The negative impact on labor-intensive firms is also significantly higher than the impact on capital-intensive firms. State-owned firms and large-scale firms are less affected than private firms or small firms. The results show that information asymmetry is an important influencing mechanism. Firms can reduce the negative influence of air pollution through active information disclosure and external media reports. We note that air pollution can also decrease firms' ESG scores by increasing firm survival risks. Our results enrich the ESG literature and contribute to the understanding of the impact of informal institutions on firms' ESG scores.