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Environments
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Air Pollution in Urban and Industrial Areas, 4th Edition
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Air Pollution in Urban and Industrial Areas, 4th Edition

A special issue of Environments (ISSN 2076-3298). This special issue belongs to the section "Environmental Monitoring and Management".

Deadline for manuscript submissions: closed (20 June 2026) | Viewed by 12679

Editors

Dr. Valerio Paolini

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Guest Editor
National Research Council of Italy, Institute of Atmospheric Pollution Research (CNR-IIA), Via Salaria 29,300, 00015 Monterotondo, Italy
Interests: air pollution; renewable energy; sustainability; environmental analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Francesco Petracchini

E-Mail Website
Guest Editor
Institute of Atmospheric Pollution Research (CNR-IIA), National Research Council, Via Salaria 29,300, 00015 Monterotondo, Italy
Interests: air pollution; renewable energy; atmosphere
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As guest editor of Environments, I invite you to submit a paper to the Special Issue, "Air Pollution in Urban and Industrial Areas, 4th Edition". Environments publishes articles and communications in the interdisciplinary area of environmental technologies and methodologies, environmental protection, and pollution prevention. Detailed information on the journal can be found at https://www.mdpi.com/journal/environments.

Airborne particle concentration levels in cities are mostly related to anthropic urban activities/sources, such as industrial and residential sectors (heating) and vehicular traffic, i.e., sources characterized by combustion processes mainly producing high levels of particulate matter (PM), sub-micrometric, and ultrafine particles. Recent epidemiological studies have demonstrated that exposure to these concentrations can lead to respiratory and circulatory health problems. The International Agency for Research on Cancer (IARC) has classified particulate matter, a major component of air pollution, as carcinogenic to humans (Group 1).

Different measures should be taken regarding vehicle technologies, distribution optimization, and regulations. Furthermore, some policies and interventions, such as the promotion of sustainable urban mobility actions (for example, different urban transport strategies ranging from carpooling and expanded electric vehicle (EV) use to bike sharing), are needed to improve urban air quality and reduce the impact of such sources on the urban environment in terms of human exposure.

Air pollution in industrial areas is still a great health and social relevance topic. In the last few decades, conventional industrial processes (e.g., concrete, steel, plastic production, waste incineration, and thermoelectric energy generation) have undergone several changes to mitigate their environmental burden. Nevertheless, such processes are still a major source of air pollutants. On the other hand, novel industrial processes related to a circular economy (waste recycling, biomaterials production, renewable energy generation, etc.) are experiencing rapid growth. At the same time, their global impact on climate change mitigation is well known, but little information is available on their local impact on air quality.

In this framework, new research is needed to provide updated information on air pollutant emissions in urban and industrial areas. Interest can be focused on regulated or emerging pollutants, including volatile organic compounds, polyaromatic, halogenated, flame retardants, siloxanes, greenhouse gases, biologically active molecules, and nanoparticles.

This Special Issue is open to the subject area of urban and industrial air pollution. The keywords listed below outline some of the possible areas of interest.

The publications in previous volumes, which we believe may be of interest to you, can be found at the following links: https://www.mdpi.com/journal/environments/special_issues/Air_Urban; https://www.mdpi.com/journal/environments/special_issues/10R20O310O; https://www.mdpi.com/journal/environments/special_issues/934N9CCWEG.

Dr. Valerio Paolini
Dr. Francesco Petracchini
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Environments is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • air pollution
  • NOx
  • particulate matter
  • residential heating
  • road traffic emissions
  • sustainable mobility
  • industrial emissions
  • pollutant dispersion
  • outdoor air quality
  • global health
  • population exposure

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Published Papers (7 papers)

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Research

28 pages, 843 KB  
Article
Stationary and Non-Stationary GEVD Models for Extreme NO2 Emissions from Eskom’s Coal-Fired Power Stations
by Mpendulo Wiseman Mamba and Delson Chikobvu
Environments 2026, 13(6), 328; https://doi.org/10.3390/environments13060328 - 9 Jun 2026
Viewed by 786
Abstract
This study uses and compares stationary and non-stationary Generalised Extreme Value Distribution (GEVD) to model the behaviour of nitrogen dioxide (NO2) emission maxima from each of 13 Eskom’s coal-fuelled power stations. The pollutant is modelled to facilitate monitoring and regulation in [...] Read more.
This study uses and compares stationary and non-stationary Generalised Extreme Value Distribution (GEVD) to model the behaviour of nitrogen dioxide (NO2) emission maxima from each of 13 Eskom’s coal-fuelled power stations. The pollutant is modelled to facilitate monitoring and regulation in order to protect public health and the environment. The Maximum Likelihood Estimate (MLE) and Generalised Maximum Likelihood Estimate (GMLE) parameter estimation methods are used and compared in finding the best-fitting model per power station. The results show that a non-stationary model with time-dependent location and/or scale parameter(s) produced the best fit for ten of the power stations, while a stationary model gave the best fit for three, as confirmed by the diagnostic tools. Future extremely high NO2 emissions were estimated by making use of the 40 and 100 quarter return levels based on the best-fitting models. This study shows how stationarity may not hold for all NO2 emission data from Eskom’s coal-fired power stations. Modelling data using time-dependent non-stationary GEVD models can be useful, especially in identifying and predicting trends or patterns in worsening high NO2 emissions with time. This modelling approach is important in providing information for planning and policy formulation of extreme emissions from coal-fired electricity-generating power stations at Eskom (South Africa). Full article
(This article belongs to the Special Issue Air Pollution in Urban and Industrial Areas, 4th Edition)
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23 pages, 5050 KB  
Article
Quantifying the Impact of Atmospheric Aerosols on Clear-Sky and All-Sky Solar Irradiance Components in a Tropical Coastal Urban Environment: A Case Study of Penang, Malaysia (2014–2018)
by Hussaini Yusuf, Norhaslinda Mohamed Tahrin and Hwee San Lim
Environments 2026, 13(5), 250; https://doi.org/10.3390/environments13050250 - 1 May 2026
Viewed by 2334
Abstract
Atmospheric aerosols strongly regulate surface solar irradiance in tropical coastal environments through scattering and absorption. This study examines aerosol–irradiance interactions over Penang, Malaysia, using Aerosol Robotic Network (AERONET) observations of aerosol optical depth (AOD), single scattering albedo (SSA), and extinction Ångström exponent (AE); [...] Read more.
Atmospheric aerosols strongly regulate surface solar irradiance in tropical coastal environments through scattering and absorption. This study examines aerosol–irradiance interactions over Penang, Malaysia, using Aerosol Robotic Network (AERONET) observations of aerosol optical depth (AOD), single scattering albedo (SSA), and extinction Ångström exponent (AE); NASA’s Prediction of Worldwide Energy Resource (POWER) irradiance data; and Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) reanalysis for aerosol compositional context. Bottom-of-atmosphere radiative forcing efficiency (BOA RFE) was quantified for global, direct and diffuse irradiance (GHI, DNI and DHI) under clear- and all-sky conditions during 2014–2018. Results show persistent aerosol-induced attenuation of surface radiation, with GHI and DNI RFE predominantly negative, while DHI RFE remains consistently positive, indicating redistribution of solar energy from direct to diffuse components. Time resolved analysis reveals daily GHI RFE typically ranging from approximately −0.5 to −3.5 W m−2 per unit AOD, with episodic excursions below −4 W m−2 per AOD during high-aerosol events, whereas DNI RFE frequently reaches values below −0.8 W m−2 per AOD, confirming its greater sensitivity to aerosol extinction. In contrast, DHI RFE commonly exceeds +5 W m−2 per AOD and intermittently surpasses +10 W m−2 per AOD, reflecting enhanced scattering and multiple-scattering effects. AOD-stratified analysis demonstrates a nonlinear weakening of forcing efficiency with increasing aerosol burden, with mean GHI RFE decreasing from approximately −1.6 to −0.4 W m−2 per AOD between low- and high-AOD regimes, accompanied by corresponding reductions in DNI (−0.35 to −0.1 W m−2 per AOD) and DHI (+3.3 to +0.8 W m−2 per AOD). Overall, aerosol loading is identified as the dominant control on BOA radiative forcing efficiency in this tropical coastal environment, while SSA and AE act as secondary modulators. Full article
(This article belongs to the Special Issue Air Pollution in Urban and Industrial Areas, 4th Edition)
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21 pages, 3969 KB  
Article
Modelling NO2 Emissions at Eskom’s Coal-Fired Power Station: Application of Statistical Distributions at Arnot
by Mpendulo Wiseman Mamba and Delson Chikobvu
Environments 2026, 13(2), 111; https://doi.org/10.3390/environments13020111 - 17 Feb 2026
Viewed by 1202
Abstract
The combustion of coal comes with a heavy price of pollutant emissions. To assist in the planning and management of these emissions and to protect human health, the current study uses the relatively heavy-tailed distributions, namely, the Weibull, Lognormal and Pareto distributions to [...] Read more.
The combustion of coal comes with a heavy price of pollutant emissions. To assist in the planning and management of these emissions and to protect human health, the current study uses the relatively heavy-tailed distributions, namely, the Weibull, Lognormal and Pareto distributions to analyse and characterise the distribution of NO2 emission (in tons) from Arnot, a coal-fired power station of South Africa’s power utility, Eskom. Quantile–quantile (QQ) plots and their corresponding derivative plots for the three distributions are used to characterise the statistical distribution of NO2 emissions. The strength and advantage of using derivative plots of the three distributions, in particular, for characterising NO2 emissions from a coal-fuelled power station, is that they are able to better capture and explain the behaviour of the data across different components of this data. Although this method possesses flexible ways of characterisation of data, it is not commonly applied to emissions data, especially NO2 emissions from a coal-fuelled power station belonging to Eskom, such as Arnot. The choice of the distributions of this study is motivated by their ability to cater to varied tails relative to the exponential distribution. Thus, the tail heaviness ranks of the distributions from lighter to heavier tail, that is, Weibull, Lognormal and Pareto, are taken into consideration in order to arrive at the best-fitting distribution(s). The Weibull distribution with a lighter tail than the Exponential distribution gave the best-fitting distribution over the Lognormal and Pareto distributions for the main body of the data. The Pareto distribution, however, captures the extreme emission tail behaviour much better than the other two distributions. The Kolmogorov–Smirnov and Vasicek–Song (VS) goodness of fit statistics were used to further assess the appropriateness of the fitted distributions. The selection of the Weibull distribution implies that milder high values and less frequent very high NO2 emission data are expected, showing the weakness of such criteria when extremes are present. For authorities to plan and draw policies for the reduction and management of emissions, these findings may be of interest to them and can assist in better understanding their behaviour and the planning to reduce the impact on humans and the environment. This may also assist practitioners in air quality modelling before other, more sophisticated methods can be explored. Full article
(This article belongs to the Special Issue Air Pollution in Urban and Industrial Areas, 4th Edition)
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12 pages, 1587 KB  
Article
Quantifying Urban Air Pollution Mitigation by Tree Canopies Using Low-Cost Sensors
by Jacopo Manzini, Yasutomo Hoshika, Barbara Baesso Moura, Pierre Sicard, Alessandra De Marco, Alessandro Zaldei, Tommaso Giordano, Bernardo Cicchi and Elena Paoletti
Environments 2026, 13(2), 97; https://doi.org/10.3390/environments13020097 - 11 Feb 2026
Cited by 1 | Viewed by 1524
Abstract
Urban environments are contaminated by a multitude of air pollutants. Tropospheric ozone (O3), nitrogen dioxide (NO2) as well as coarse particulate matter (PM10) and fine particulate matter (PM2.5) are the most dangerous for human health. [...] Read more.
Urban environments are contaminated by a multitude of air pollutants. Tropospheric ozone (O3), nitrogen dioxide (NO2) as well as coarse particulate matter (PM10) and fine particulate matter (PM2.5) are the most dangerous for human health. However, urban greenery, in particular trees, offer a variety of ecosystem services, including the ability to improve air quality. We planted 170 young trees in the city of Florence using five species with proven capabilities to remove air pollutants, and open-field research was conducted to assess their pollution removal potential. Multi-sensor monitoring devices were used to monitor air pollutant concentrations and meteorological parameters from the first three years after planting. The devices were installed inside/outside the plantation and above/below the canopies. The experiment showed that the selection of suitable species effectively led to an improvement in air quality, with a reduction in monitored air pollutants below the canopy. In detail, a reduction in O3 and NO2 was detected for the second (2023) and third (2024) growing seasons, while a reduction in PM10 was only observed in 2024. The highest average reduction percentage was found for O3 (−9.1%) and PM10 (−24.5%) during 2023 and 2024, respectively. These findings highlight that nature-based solutions are really effective in air pollution mitigation, suggesting their implementation to further expand urban reforestation programmes and preserve human health. Full article
(This article belongs to the Special Issue Air Pollution in Urban and Industrial Areas, 4th Edition)
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14 pages, 1112 KB  
Article
Selecting Non-VOC Emitting Cork Oaks—A Chance to Reduce Regional Air Pollution
by Michael Staudt, Meltem Erdogan and Coralie Rivet
Environments 2026, 13(2), 70; https://doi.org/10.3390/environments13020070 - 25 Jan 2026
Viewed by 1618
Abstract
Cork oak is a strong emitter of volatiles, namely monoterpenes, which are important precursors of secondary air pollutants. Past studies have revealed distinct chemotypes in emitting as well as non-emitting individuals. Promoting non-emitters in afforestation and urban greening could improve air quality, but [...] Read more.
Cork oak is a strong emitter of volatiles, namely monoterpenes, which are important precursors of secondary air pollutants. Past studies have revealed distinct chemotypes in emitting as well as non-emitting individuals. Promoting non-emitters in afforestation and urban greening could improve air quality, but their rarity suggests that they are less resilient. To gain insight into this, we screened natural descendants from two non-emitting cork oaks for emissions and ecophysiological traits (CO2/H2O-gas exchange variables, budburst date, growth) and tested whether emitting and non-emitting descendants differ in their resistance to temperature and light fluctuations (sun-flecks). Both half-sib populations were composed of the same chemotypes in similar frequencies, comprising 32% of non-emitters and 50 and 18% of two emitting chemotypes with overall moderate emission rates. Based on this distribution, we identified an inheritance mode and compared it with the chemotype frequency of the mother population. In terms of ecophysiological traits, all chemotypes performed similarly, and non-emitters were as resistant to sun-flecks as emitters. We conclude that the chemotypes in emitters reflect a common polymorphism in monoterpene-emitting plants that is not related to adaptive selection. We also conclude that non-emission is heritable and that its phenotype should be evaluated in reforestation studies. Full article
(This article belongs to the Special Issue Air Pollution in Urban and Industrial Areas, 4th Edition)
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25 pages, 4156 KB  
Article
Monitoring Industrial VOC Emissions and Geospatial Analysis
by Sebastian Barbu Barbes, Ana Cornelia Badea and Vlad Iordache
Environments 2026, 13(1), 41; https://doi.org/10.3390/environments13010041 - 8 Jan 2026
Viewed by 1641
Abstract
Volatile organic compounds (VOCs) emissions from petroleum product storage pose not only a significant environmental concern but also a potential threat to occupational health. This study investigates geospatial analysis of VOCs on an industrial platform in Romania, utilizing a combination of portable field [...] Read more.
Volatile organic compounds (VOCs) emissions from petroleum product storage pose not only a significant environmental concern but also a potential threat to occupational health. This study investigates geospatial analysis of VOCs on an industrial platform in Romania, utilizing a combination of portable field detectors and geostatistical modeling techniques. For more than 10 months, we conducted measurements at 41 georeferenced sampling points across three operational zones, using FID/PID instruments calibrated and validated in accordance with national standards. To evaluate dispersion conditions, meteorological data were simultaneously collected. VOC concentrations were measured under varying meteorological scenarios and analyzed using the Empirical Bayesian Kriging (EBK) method in ArcGIS Pro 3.1.0. Maximum concentrations reached up to 229.46 mg/m3 in central tank areas, with some point samples exceeding this level. Peripheral zones generally showed values below 65 mg/m3, although concentrations above 100 mg/m3 were still observed at 10% of the monitoring sites. The results indicate apparent spatial clustering of elevated VOC levels, particularly under low wind speed and high humidity. Our study highlights the relevance of continuous monitoring and site-specific mitigation strategies in high-risk industrial settings in Romania. Full article
(This article belongs to the Special Issue Air Pollution in Urban and Industrial Areas, 4th Edition)
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29 pages, 3257 KB  
Article
Modeling Air Pollution from Urban Transport and Strategies for Transitioning to Eco-Friendly Mobility in Urban Environments
by Sayagul Zhaparova, Monika Kulisz, Nurzhan Kospanov, Anar Ibrayeva, Zulfiya Bayazitova and Aigul Kurmanbayeva
Environments 2025, 12(11), 411; https://doi.org/10.3390/environments12110411 - 1 Nov 2025
Cited by 3 | Viewed by 2644
Abstract
Urban air pollution caused by vehicular emissions remains one of the most pressing environmental challenges, negatively affecting both public health and climate processes. In Kokshetau, Kazakhstan, where electric vehicle (EV) adoption accounts for only 0.019% of the total fleet and charging infrastructure is [...] Read more.
Urban air pollution caused by vehicular emissions remains one of the most pressing environmental challenges, negatively affecting both public health and climate processes. In Kokshetau, Kazakhstan, where electric vehicle (EV) adoption accounts for only 0.019% of the total fleet and charging infrastructure is nearly absent, reducing transport-related emissions requires short-term and cost-effective solutions. This study proposes an integrated approach combining urban ecology principles with computational modeling to optimize traffic signal control for emission reduction. An artificial neural network (ANN) was trained using intersection-specific traffic data to predict emissions of carbon monoxide (CO), nitrogen oxides (NOx), sulfur dioxide (SO2), and particulate matter (PM2.5). The ANN was incorporated into a nonlinear optimization framework to determine traffic signal timings that minimize total emissions without increasing traffic delays. The results demonstrate reductions in emissions of CO by 12.4%, NOx by 9.8%, SO2 by 7.6%, and PM2.5 by 10.3% at major congestion hotspots. These findings highlight the potential of the proposed framework to improve urban air quality, reduce ecological risks, and support sustainable transport planning. The method is scalable and adaptable to other cities with similar urban and environmental characteristics, facilitating the transition toward eco-friendly mobility and integrating data-driven traffic management into broader climate and public health policies. Full article
(This article belongs to the Special Issue Air Pollution in Urban and Industrial Areas, 4th Edition)
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