Air Pollution In Cities Research Articles

The Synergy Between CO2 and Air Pollution Emissions in Chinese Cities by 2060: An Assessment Based on the Emissions Inventory and Dynamic Projection Model

Synergizing air pollution control and climate change mitigation has been of significant academic and policy concern. The synergy between air pollution and carbon emissions is one of the measures to understand the characteristics and process of the air pollution–carbon synergistic control, which will also provide valuable information for collaboratively achieving Sustainable Development Goals (SDGs) (such as SDGs 11 and 13). This study establishes a systematic framework integrating emissions inventory and projection models, correlation mining and typology analysis methods to predictively evaluate the synergy and comprehensive coordination between air pollution and carbon dioxide (CO2) emissions in Chinese cities by 2030, 2050, and 2060 under different policy scenarios for air pollution and CO2 emissions control. The results reveal the significant effects of synergistically implementing clean air and aggressive carbon-reducing policies on mitigating air pollution and CO2 emissions. Under the On-time Peak-Net Zero-Clean Air and Early Peak-Net Zero-Clean Air scenarios, the total reduction and synergy for air pollution and CO2 emissions will be more significant, particularly by 2050 and 2060. This study is the first to integrate scenario projection and synergy evaluation in air pollution and CO2 research, providing a novel supplement to the air pollution–climate change synergy methodology based on co-benefit estimation. The methods and findings will also contribute to measuring the achievement and analyzing the interaction of the SDGs.

Using EEA data to calculate health impacts of air pollution via the AirQ+ model: a practical example

Abstract The AirQ+ software tool for health risk assessment of air pollution, developed by the World Health Organization, helps quantify the effects of exposure to air pollution. Specifically, AirQ+ can estimate the effects of short-term changes in air pollution (based on risk estimates from time-series studies) and the effects of long-term exposures (using life-tables approach and based on risk estimates from cohort studies). AirQ+ is available in English, French, German and Russian. In this presentation, AirQ+ will be loaded with air pollution data from the European Environment Agency datasets for a quick evaluation of health effects from air pollution in an European city.

Evaluation of Ecological Service Functions of Urban Greening Tree Species in Northern China Based on the Species-Specific Air Purification Index

Urban forests, as an integral part of nature-based solutions (NBS), are significant contributors to improving urban air quality, delivering ecological service functions and environmental benefits to human health and well-being. Suitable urban forest management, including proper species selection, needs to be defined to efficiently reduce air pollutants in cities, with a focus on the removal ability of the main air pollutants (PM2.5, PM10, O3, and NO2), the ecological adaptability to O3 and NO2, and allergenic effects. This study ranked 73 urban greening tree species in northern Chinese cities based on their ability to maximize air quality and minimize disservices. This study proposed a novel Species-Specific Air Purification Index (S-API), which is suitable for air quality improvement for tree/shrub species. Urban managers are recommended to select species with an S-API > 1.47—that is, species that have a high removal capacity of PM2.5, PM10, O3, and NO2, are O3- and NO2-tolerant, and are non-allergenic (e.g., Castanea mollissima Blume, Ginkgo biloba L., Hibiscus syriacus L., Ilex chinensis Sims, Juniperus procumbens (Endl.) Iwata et Kusaka, Liriodendron chinense (Hemsl.) Sarg., Morus alba L., Styphnolobium japonicum (L.) Schott, Syringa oblata Lindl., and Ulmus pumila L.). The S-API of urban greening species thus represents a potentially useful metric for air pollutant risk assessment and for selecting appropriate species for urban greening in cities facing serious air pollution challenges.

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.

Rectifying local governments’ strategic environmental enforcement: Can refined air pollution monitoring enhance local air quality in China?

Insufficient local environmental enforcement has become a pivotal economic and political concern in China. In 2017, the Chinese central government introduced the Air Pollution Hot-Spot Grid (APHG) monitoring program. However, little is known about the consequences. This study investigates in detail how this monitoring program affects city-level air pollution by applying the difference-in-differences method. This program was responsible for a notable decrease in air pollution at the city level. This decrease was mostly the result of stronger local environmental governance, advances in green innovation, and an upgraded industrial structure. Additionally, an examination of heterogeneous treatment effects across different cities revealed that the most substantial effects were observed in large, non-resource-based, and eastern cities. Additional analysis reveals the presence of a spillover effect, whereby the adoption of this program in one city helps reduce air pollution in nearby cities. These results highlight the significance of refined air pollution monitoring for addressing imperfect local environmental enforcement.

Achieving deep transport energy demand reductions in the United Kingdom

The transport sector is a crucial yet challenging area to decarbonize, given its heavy reliance on fossil fuel usage, carbon-intensive infrastructure and car-centric lifestyles. It remains the largest contributor to local air pollution in cities yet has the potential to improve people's physical and mental health. This research investigated the potential contribution of transport energy demand reduction to climate change mitigation and improving public health. Using a comprehensive bottom-up modelling framework, the Transport Energy and Air pollution Model (TEAM), this study provides an integrated assessment of the impacts of deep mobility-related energy demand reductions, including lifecycle carbon emissions, local air pollution and health impacts. Using a sociotechnical scenario approach and the UK as a case study, this research reveals that energy demand reductions of up to 61 % by 2050 compared to baseline levels are achievable and can enhance citizens' quality of life. Business as usual approaches which rely on a technical transition miss the legislated carbon budgets and result in higher energy demand in 2050. More comprehensive scenarios deliver a reduction of up to 72 % in total lifecycle carbon emissions by 2050, with approximately half of the reduction achieved through mode shifting and avoiding travel, while the other half comes from vehicle energy efficiency, electrification, and downsizing of the vehicle fleets. The research shows that it can lead to significant co-benefits such as improved local air pollution and public health. The feasibility and practicality of policy measures and integrated strategies identified for achieving deep transport-energy demand reductions are discussed.

Air Quality Prediction using Ensemble Classifiers and Single Decision Tree

Air pollution is a major concern nowadays that needs immediate action. Various acts have already been initiated by the government in controlling it. The harmful gases present in the air are Nitrogen dioxide, Sulphur dioxide, Ozone, and Carbon monoxide that is causing air pollution and are becoming the major cause of harmful diseases. It does not affect human beings only but the entire environment. According to World Health Organization, air pollution is killing seven million people around the world. Planting trees and avoiding using plastics is the possible solution to control air pollution. The air quality index is the measure through which we categorize the air pollution of various cities. In this paper, we will discuss the bagging and boosting method, the extra trees method of machine learning in predicting air quality index, and its comparison with Decision trees. The dataset is collected from the Open Weather Application Program Interface. In this, the prediction of the Air Quality Index is done based on previous data. Time i.e., hourly prediction and space series prediction are done on the dataset. After implementing each algorithm confusion matrix is obtained and from this precision and recall are calculated for each category of the Air quality index ranging from one to five with one being good and five being very unhealthy. It has been observed that Gradient Boosting achieves the highest accuracy with 98.89%.

Evidence-driven indoor air quality improvement: An innovative and interdisciplinary approach to improving indoor air quality.

Indoor air pollution is a recognized emerging threat, claiming millions of lives annually. People are constantly exposed to ambient and indoor air pollution. The latest research shows that people in developed countries spend up to 90% of their time indoors and almost 70% at home. Although impaired IAQ represents a significant health risk, it affects people differently, and specific populations are more vulnerable: children, the elderly, and people with respiratory illnesses are more sensitive to these environmental risks. Despite rather extensive research on IAQ, most of the current understanding about the subject, which includes pollution sources, indoor-outdoor relationships, and ventilation/filtration, is still quite limited, mainly because air quality monitoring in the EU is primarily focused on ambient air quality and regulatory requirements are lacking for indoor environments. Therefore, the EDIAQI project aims to improve guidelines and awareness for advancing the IAQ in Europe and beyond by allowing user-friendly access to information about indoor air pollution exposures, sources, and related risk factors. The solution proposed with EDIAQI consists of conducting a characterization of sources and routes of exposure and dispersion of chemical, biological, and emerging indoor air pollution in multiple cities in the EU. The project will deploy cost-effective/user-friendly monitoring solutions to create new knowledge on sources, exposure routes, and indoor multipollutant body burdens. The EDIAQI project brings together 18 organizations from 11 different European countries that provide interdisciplinary skills and expertise in various fields, including environmental science and technology, medicine, and toxicology, as well as policy design and public engagement.

Recent Trends in Air Pollution in the Most Important City of The Romanian Black Sea Coast

Air pollution is one of the greatest environmental issues of contemporary society, affecting virtually all major cities worldwide, including those in Romania. Consequently, evaluating the dynamics of pollution conditions in urban environments is essential for designing control measures and adaptation strategies to this environmental disruption. This paper aims to analyze the recent dynamics of pollution and air quality in Constanta, the largest city on the Romanian coastline and one of the most important cities along the entire Black Sea coast. The investigations were based on the official concentrations of major atmospheric pollutants recorded at air quality monitoring stations located within or near Constanta. The dynamics of pollutant concentrations were explored at annual and seasonal levels, and the results showed a mixed picture of changes in the atmospheric pollutant concentrations, in recent years. Essentially, the findings highlighted both increases and decreases in air pollution conditions, emphasizing the need for constant air quality monitoring and controlling throughout Constanta city.

Air Pollution in the Port City of Lithuania: Characteristics of the Distribution of Nitrogen Dioxide and Solid Particles When Assessing the Demographic Distribution of the Population

Air Pollution in the Port City of Lithuania: Characteristics of the Distribution of Nitrogen Dioxide and Solid Particles When Assessing the Demographic Distribution of the Population

Does high-speed rail construction reduce air pollution? Evidence from prefecture-level cities in China

Based on data from 282 cities in China, this study assesses the impact of high-speed rail (HSR) construction on air pollution with a difference-in-difference (DID) method and a quasi-natural experiment of HSR construction. The results indicate HSR construction reduces air pollution, specifically, PM2.5 concentrations in HSR cities decreased by 2.91 %. Moreover, heterogeneity exists in the influence of HSR on air pollution of different types of cities. Considering cities of different administration levels, HSR construction significantly reduces air pollution in peripheral cities, while the pollution reduction in core cities is not obvious. Considering cities with different population sizes, the HSR construction suppresses air pollution in small cities and big cities, while this suppressive effect in small cities is more obvious. The mechanism analysis confirms HSR construction reduces PM2.5 by three ways: substituting civil aviation, adjusting industrial structure, and enhancing innovation foundation. Meanwhile, a spatial spillover effect exists in HSR's effect on air pollution: HSR construction reduces air pollution in local cities, while also promoting air quality in neighboring cities due to the spatial correlation between cities.

Air pollution and grouping of cities according to their environmental status

In the article, the ecological situation of the cities located in the Kura-Araz plain is analyzed. Our study area is the most populated and most important agricultural region in the country. The possibility of environmental pollution is also high in the area located on the east- west roads. In particular, the pollutants released into the atmosphere were analyzed and the results were determined. During the research, the atmospheric pollution in the cities during the historical period was studied using the mathematical-statistical method. Ecological zoning of cities was carried out using GIS technologies. The main source of air pollution in cities is cars, their number, and the use of outdated cars. Another reason is the important industrial cities located in the study area. Substances released into the air from industrial enterprises pollute the atmosphere of the cities. It was determined that the environmental situation is more intense in cities where large industrial enterprises are located. There are large, medium and small cities in the study area. The main polluting cities are the cities of Mingachevir and Shirvan, which belong to the group of large and medium cities because the most important industrial enterprises of the country are located here. Also, these cities are important power centers of the country. These cities differ from others in terms of population. At the same time, we have included the city of Yevlakh in the group of cities with an important role in atmospheric pollution. As a result of the analysis, ecological zoning of cities was carried out. At this time, 3 gradations were used. During this zoning, the pollution of the atmosphere was taken into account. The influence of carbon monoxide CO2, carbon oxides CHx, nitrogen oxides NOx and sulfur oxides SOx on atmospheric air in cities was analyzed. The main cause of pollution is the location at the intersection of highways. The location on the east-west roads has made the ecological situation in the cities even more tense. Especially in Mingachevir and Shirvan, the environmental situation is more intense.

Comprehensive Research Study of Challenges and Opportunities in Electric Vehicle Adoption: Case Study in Jakarta

This article delves into the potential of electric vehicle (EV) adoption in Central Jakarta, a densely populated area grappling with significant urban and transportation challenges. Trams are an environmentally friendly mode of transport that is environmentally friendly, efficient, comfortable, accessibility, and enhances the aesthetic value of the city. Trams do not produce exhaust emissions, so they can help reduce air pollution in big cities. This is important for improving air quality and public health. The research variables include cost implications, environmental impact, and other relevant aspects of transitioning from fuel-based cars and motorcycles to electric vehicles. The research extensively explores various reputable sources such as papers, journals, and research studies relevant to the chosen research variables, enhancing the credibility of the findings. The study reveals that while the adoption of EVs presents several opportunities, such as reducing traffic congestion and environmental pollution, it also poses significant challenges. These include high costs of EVs, lack of charging infrastructure, and consumers' unfamiliarity with the technology. The research suggests that addressing these challenges requires an integrated and comprehensive solution, including policy measures such as increasing public charging infrastructure, transforming tax schemes, and providing incentives for EV users. This research contributes to the ongoing discourse on sustainable urban transportation, providing valuable insights for policymakers, academia, and the local community in Jakarta and beyond.

Initial Insights into Teleworking’s Effect on Air Quality in Madrid City

Commuting to work by private vehicle is one of the main sources of air pollution in cities, mainly from NO2 and particulate matter (PM2.5 and PM10). With the spread of telework, traffic congestion during peak hours is reduced on certain days of the week, improving air quality. This study analyzes the relationship between the improvement of air quality and urban traffic resulting from teleworking activities after the COVID-19 pandemic in Madrid, Spain. This article considers road traffic and teleworking before the COVID-19 pandemic (2018 and 2019), during the pandemic (2020 and 2021) and in the period after (2022 and 2023) in the city center and the influence on certain environmental factors. Daily NO2, PM2.5, PM10, and O3 concentration data were collected at air quality stations in Madrid municipality, and traffic data and some meteorological variables such as wind speed, precipitation and temperature were considered. When conducting correlation and regression analysis among the variables, there is a clear association between NO2 and traffic before the pandemic, which is lower for both PM and O3. This correlation was maintained during the pandemic, except for O3, the association of which increased during this period and then decreased in the later period due to various motives. These results seem to indicate the existence of a relevant relationship between urban mobility and air quality and an especially relevant relationship with telework, suggesting the need for policies aimed at promoting sustainable mobility in the future.

Environmental regulation and worker earnings: Evidence from city-level air quality standards in China

This study delves into the influence of environmental regulations on worker earnings, employing China's City Air Pollution Prevention and Control Program (CAPPCP) as a quasi-experimental framework. We employ a difference-in-differences (DD) approach and match two unique firm-level data sets that include information on firm's economic indicators, and various emissions and abatement equipment. The results show that CAPPCP causes an average of 3.2% reduction in worker earnings, which amounts to CNY 6.1 billion (USD 803.4 million) in forgone earnings. The estimated result is less than that of comparable US studies. In addition, the reduction in earning tends to be attributed at bonuses rather than regular wages, and firms pay more unemployment insurance for employees. In the channel test, CAPPCP leads to an increase of 4.7% in firm's pollution abatement equipment and 6.4% in pollution treatment capacity, which significantly increase firm's production costs. We also find different types of firm responses to the regulations, where high-polluting, smaller, labor-intensive and non-SOE firms show a higher reduction in worker earnings than their counterparts. A cost-benefit analysis suggests that the health benefits gained through CAPPCP significantly outweigh that of earning losses.

Ship deployment problem with green technology adoption for an inland river carrier under non-identical streamflow and speed limits

The maritime industry is currently experiencing a shift towards green corridors. The inland waterway is the main transportation corridor with a relatively low adoption of green technologies, which produces heavy air pollution in inland cities. This paper investigates the ship deployment problem for an inland river carrier considering the adoption of green fuel and green technologies for a given ship fleet. A non-linear programming (NLP) model is proposed to jointly optimize green technology adoption, sailing speed, and routing problems while considering two unique characteristics of inland river shipping, including the non-identical streamflow and the speed limits on each shipping leg. The analytical propositions reveal the optimal operation strategy of the inland river ship fleet, and a column generation-based algorithm is further designed to solve the proposed model. By jointly investigating the ship operation strategy and sulphur emissions, we find the following management insights: Firstly, the ship would have a lower willingness to invest in shore power (scrubber) if it installed scrubber (shore power). The higher streamflow and looser speed limits would reduce the spillover effect of green technology adoption. Secondly, emissions will spillover from ports to river legs if only shore power is provided. Our findings provide valuable insights for policymakers to promote complete green technology adoption to achieve comprehensive abatement of the entire inland river.

Sources and Specified Health Risks of 12 PM2.5-Bound Metals in a Typical Air-Polluted City in Northern China during the 13th Five-Year Plan.

The continuous monitoring of PM2.5 (including 12 metal elements) was conducted in Jinan, a city with poor air quality in China, during the 13th Five-Year Plan (2016-2020). Positive matrix factorization (PMF) was used to identify emission sources of PM2.5-bound metals, and the health risks of the metals and their emission sources were assessed. During the study period, the concentration of most metals showed a decreasing trend (except Al and Be), and a significant seasonal difference was found: winter > fall > spring > summer. The PMF analysis showed that there were four main sources of PM2.5-bound metals, and their contributions to the total metals (TMs) were dust emissions (54.3%), coal combustion and industrial emissions (22.3%), vehicle emissions (19.3%), and domestic emissions (4.1%). The results of the health risk assessment indicated that the carcinogenic risk of metals (Cr and As) exceeded the acceptable level (1 × 10-6), which was of concern. Under the influence of emission reduction measures, the contribution of emission sources to health risks changes dynamically, and the emission sources that contribute more to health risks were coal combustion and industrial emissions, as well as vehicle emissions. In addition, our findings suggest that a series of emission reduction measures effectively reduced the health risk from emission sources of PM2.5-bound metals.

Can Energy-Consuming Rights Trading Policies Help to Curb Air Pollution? Evidence from China

Energy-consuming rights trading policies (ECRTPs) represent a significant institutional innovation for China aimed at achieving the dual control targets of total energy consumption and energy consumption intensity. However, the effectiveness of these policies in curbing air pollution remains uncertain. This study treats ECRTPs as a quasi-natural experiment to empirically analyze their impact on air pollution, utilizing panel data encompassing 277 prefecture-level cities in China covering the period from 2011 to 2021. Analytical methods applied include a Difference-in-Differences model, a mediation effects model, and a triple differences model to explore the effects of ECRTPs on air pollution. The findings reveal that ECRTP can significantly suppress air pollution, and this conclusion remains valid even after conducting robustness tests. Mechanism analysis indicates that ECRTPs suppress air pollution by boosting energy efficiency, advancing industrial structure upgrading, and facilitating technological innovation. Further heterogeneous studies show that ECRTPs have a more pronounced inhibitory effect on air pollution in cities that are economically and socially developed, exhibit greater energy-saving potential, are characterized as resource-based cities, and serve as key regions for the prevention and control of air pollution. The research conclusion provides empirical evidence and policy implications for evaluating the environmental effects of ECRTPs and further improving China’s energy-consuming rights trading system, as well as offering references and guidance for other developing countries to put forward ECRTPs.

Probing the capacity of a spatiotemporal deep learning model for short-term PM2.5 forecasts in a coastal urban area

Accurate forecast of fine particulate matter (PM2.5) is crucial for city air pollution control, yet remains challenging due to the complex urban atmospheric chemical and physical processes. Recently deep learning has been routinely applied for better urban PM2.5 forecasts. However, their capacity to represent the spatiotemporal urban atmospheric processes remains underexplored, especially compared with traditional approaches such as chemistry-transport models (CTMs) and shallow statistical methods other than deep learning. Here we probe such urban-scale representation capacity of a spatiotemporal deep learning (STDL) model for 24-hour short-term PM2.5 forecasts at six urban stations in Rizhao, a coastal city in China. Compared with two operational CTMs and three statistical models, the STDL model shows its superiority with improvements in all five evaluation metrics, notably in root mean square error (RMSE) for forecasts at lead times within 12 h with reductions of 49.8 % and 47.8 % respectively. This demonstrates the STDL model's capacity to represent nonlinear small-scale phenomena such as street-level emissions and urban meteorology that are in general not well represented in either CTMs or shallow statistical models. This gain of small-scale representation in forecast performance decreases at increasing lead times, leading to similar RMSEs to the statistical methods (linear shallow representations) at about 12 h and to the CTMs (mesoscale representations) at 24 h. The STDL model performs especially well in winter, when complex urban physical and chemical processes dominate the frequent severe air pollution, and in moisture conditions fostering hygroscopic growth of particles. The DL-based PM2.5 forecasts align with observed trends under various humidity and wind conditions. Such investigation into the potential and limitations of deep learning representation for urban PM2.5 forecasting could hopefully inspire further fusion of distinct representations from CTMs and deep networks to break the conventional limits of short-term PM2.5 forecasts.

Behavioural attitudes of three-wheeled taxi drivers towards mitigation of on-road air pollution in urban cities in Sri Lanka: a qualitative study from South Asia

ObjectivesIn recent years, urban areas across South Asia have experienced alarming levels of air pollution, primarily attributed to the rapid growth of motorised transportation. Three-wheeled taxis, commonly known as ‘tuk-tuks’,...