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Environments
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Air Pollution in Urban and Industrial Areas
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Air Pollution in Urban and Industrial Areas

A special issue of Environments (ISSN 2076-3298).

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 19996

Special Issue Editors

Dr. Francesco Petracchini

E-Mail Website
Guest Editor
Consiglio Nazionale delle Ricerche, Rome, Italy
Dr. Valerio Paolini

E-Mail Website
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. Valeria Rizza

E-Mail Website
Guest Editor
Consiglio Nazionale delle Ricerche, Rome, Italy
Interests: air quality monitoting; atmospheric dispersion modeling; health effect

Special Issue Information

Dear Colleagues,

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

According to the European Environment Agency’s 2019 Air Quality Report, cities represent an environment characterized by many emission sources that significantly contribute to the daily total particle exposure for humans. Due to population growth, the economy and industrial production are concentrated in urban areas, which leads to a high level of urban transport. In particular, urban freight transport is another contributor to greenhouse gas emissions and air pollution.

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 (such as different urban transport strategies ranging from car-pooling, expanded electric vehicle (EV) use to bike-sharing) are needed to improve urban air quality and to reduce the impact of such sources on the urban environment in terms of human exposure.

Air pollution in industrial areas is still a topic of great health and social relevance. In the last few decades, conventional industrial processes (e.g., concrete, steel, and 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; while their global impact on climate change mitigation is well known, 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 pollutants 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 provide an outline of some of the possible areas of interest.

Dr. Francesco Petracchini
Dr. Valerio Paolini
Dr. Valeria Rizza
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 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind 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 (5 papers)

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Research

21 pages, 10255 KiB  
Article
Design of Machine Learning Prediction System Based on the Internet of Things Framework for Monitoring Fine PM Concentrations
by Shun-Yuan Wang, Wen-Bin Lin and Yu-Chieh Shu
Environments 2021, 8(10), 99; https://doi.org/10.3390/environments8100099 - 24 Sep 2021
Cited by 7 | Viewed by 3092
Abstract
In this study, a mobile air pollution sensing unit based on the Internet of Things framework was designed for monitoring the concentration of fine particulate matter in three urban areas. This unit was developed using the NodeMCU-32S microcontroller, PMS5003-G5 (particulate matter sensing module), [...] Read more.
In this study, a mobile air pollution sensing unit based on the Internet of Things framework was designed for monitoring the concentration of fine particulate matter in three urban areas. This unit was developed using the NodeMCU-32S microcontroller, PMS5003-G5 (particulate matter sensing module), and Ublox NEO-6M V2 (GPS positioning module). The sensing unit transmits data of the particulate matter concentration and coordinates of a polluted location to the backend server through 3G and 4G telecommunication networks for data collection. This system will complement the government’s PM2.5 data acquisition system. Mobile monitoring stations meet the air pollution monitoring needs of some areas that require special observation. For example, an AIoT development system will be installed. At intersections with intensive traffic, it can be used as a reference for government transportation departments or environmental inspection departments for environmental quality monitoring or evacuation of traffic flow. Furthermore, the particulate matter distributions in three areas, namely Xinzhuang, Sanchong, and Luzhou Districts, which are all in New Taipei City of Taiwan, were estimated using machine learning models, the data of stationary monitoring stations, and the measurements of the mobile sensing system proposed in this study. Four types of learning models were trained, namely the decision tree, random forest, multilayer perceptron, and radial basis function neural network, and their prediction results were evaluated. The root mean square error was used as the performance indicator, and the learning results indicate that the random forest model outperforms the other models for both the training and testing sets. To examine the generalizability of the learning models, the models were verified in relation to data measured on three days: 15 February, 28 February, and 1 March 2019. A comparison between the model predicted and the measured data indicates that the random forest model provides the most stable and accurate prediction values and could clearly present the distribution of highly polluted areas. The results of these models are visualized in the form of maps by using a web application. The maps allow users to understand the distribution of polluted areas intuitively. Full article
(This article belongs to the Special Issue Air Pollution in Urban and Industrial Areas)
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13 pages, 15157 KiB  
Article
Atmospheric Contamination of Coastal Cities by the Exhaust Emissions of Docked Marine Vessels: The Case of Tromsø
by Asier Zubiaga, Synne Madsen, Hassan Khawaja and Gernot Boiger
Environments 2021, 8(9), 88; https://doi.org/10.3390/environments8090088 - 3 Sep 2021
Cited by 2 | Viewed by 2773
Abstract
Docked ships are a source of contamination for the city while they keep their engine working. Plume emissions from large boats can carry a number of pollutants to nearby cities causing a detrimental effect on the life quality and health of local citizens [...] Read more.
Docked ships are a source of contamination for the city while they keep their engine working. Plume emissions from large boats can carry a number of pollutants to nearby cities causing a detrimental effect on the life quality and health of local citizens and ecosystems. A computational fluid dynamics model of the harbour area of Tromsø has been built in order to model the deposition of CO2 gas emitted by docked vessels within the city. The ground level distribution of the emitted gas has been obtained and the influence of the wind speed and direction, vessel chimney height, ambient temperature and exhaust gas temperature have been studied. The deposition range is found to be the largest when the wind speed is low. At high wind speeds, the deposition of pollutants along the wind direction is enhanced and spots of high pollutant concentration can be created. The simulation model is intended for the detailed study of the contamination in cities near the coast or an industrial pollutant source of any type of gas pollutant and can easily be extended for the study of particulate matter. Full article
(This article belongs to the Special Issue Air Pollution in Urban and Industrial Areas)
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11 pages, 2140 KiB  
Article
Visibility Driven Perception and Regulation of Air Pollution in Hong Kong, 1968–2020
by Peter Brimblecombe
Environments 2021, 8(6), 51; https://doi.org/10.3390/environments8060051 - 4 Jun 2021
Cited by 9 | Viewed by 4515
Abstract
Visibility is a perceptible indicator of air pollution, so it is hardly surprising that it has been used to promote the regulation of air pollutants. In Hong Kong, poor visibility associated with air pollution has been linked with changes in tourist choices and [...] Read more.
Visibility is a perceptible indicator of air pollution, so it is hardly surprising that it has been used to promote the regulation of air pollutants. In Hong Kong, poor visibility associated with air pollution has been linked with changes in tourist choices and health outcomes. Much research is available to examine the early deterioration of visibility in the city, and especially its relation to particulate sulfate. The period 2004–2012 saw especially poor visibility in Hong Kong and coincided with a time when pollutant levels were high. There is a reasonable correlation (multiple r2 = 0.57) between the monthly hours of low visibility (<8 km) and PM10, NO2, SO2, and O3 concentrations from the late 1990s. Visibility can thus be justified as a route to perceiving air pollution. Over the last decade, visibility has improved and average pollutant concentrations have declined in Hong Kong. The changing health risk from individual pollutants parallels their concentration trends: the risk from NO2 and particulate matter at urban sites has declined, but there have been increases in the health risks from ozone as its concentrations have risen across the region, although this is dominated by concentration increases at more rural sites. Since 2004, the frequency of search terms such as visibility, air pollution, and haze on Google has decreased in line with improved visibility. Despite positive changes to Hong Kong’s air quality, typically, the media representation and public perception see the situation as growing more severe, possibly because attention focuses on the air quality objectives in Hong Kong being less stringent than World Health Organisation guidelines. Policymakers increasingly need to account for the perceptions of stakeholders and acknowledge that these are not necessarily bound to measurements from monitoring networks. Improvements in air quality are hard won, but conveying the nature of such improvements to the public can be an additional struggle. Full article
(This article belongs to the Special Issue Air Pollution in Urban and Industrial Areas)
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13 pages, 2209 KiB  
Article
Detecting Leaders Country from Road Transport Emission Time-Series
by Giovanni De Luca and Federica Pizzolante
Environments 2021, 8(3), 18; https://doi.org/10.3390/environments8030018 - 27 Feb 2021
Cited by 8 | Viewed by 3325
Abstract
Nowadays, climate change and global warming have become the main concerns worldwide. One of the main causes are the greenhouse gas (GHG) emissions produced by human activities, especially by the transportation sector. The adherence to international agreements and the implementation of climate change [...] Read more.
Nowadays, climate change and global warming have become the main concerns worldwide. One of the main causes are the greenhouse gas (GHG) emissions produced by human activities, especially by the transportation sector. The adherence to international agreements and the implementation of climate change policy are necessary conditions for reducing environmental problems. This paper investigates the lead–lag relationship between Organization for Economic Co-operation and Development (OECD) and Annex I member countries on road transport emission performance focusing on the statistical analysis of the lead–lag relationships between the road transport emission time-series from 1970–2018 extracted by the Emissions Database for Global Atmospheric Research (EDGAR) database. The analysis was carried out using the cross-correlation function between each pair of the countries’ time-series considered. Empirical results confirm that some nations have been playing a role as leaders, while others as followers. Sweden can be considered the leader, followed by Germany and France. By analyzing their environmental policy history, we can figure out a common point that explains our results. Full article
(This article belongs to the Special Issue Air Pollution in Urban and Industrial Areas)
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20 pages, 9944 KiB  
Article
Chemical Composition of Bulk Precipitation and Its Toxicity Potential Index in the Metropolitan Area of Monterrey, Northeastern Mexico
by Darithsa Loya-González, Daniel López-Serna, Juan Manuel Alfaro-Barbosa, Antonio López-Reyes, Humberto González-Rodríguez and Israel Cantú-Silva
Environments 2020, 7(12), 106; https://doi.org/10.3390/environments7120106 - 8 Dec 2020
Cited by 6 | Viewed by 4294
Abstract
The rainwater chemistry within the Metropolitan Area of Monterrey (MAM) was studied during a one-year period (January 2019–January 2020) in seven sampling sites. The metal concentration of Zn, Fe, Cd, Cu, Ni, and Mn was analyzed in bulk samples and the toxicity potential [...] Read more.
The rainwater chemistry within the Metropolitan Area of Monterrey (MAM) was studied during a one-year period (January 2019–January 2020) in seven sampling sites. The metal concentration of Zn, Fe, Cd, Cu, Ni, and Mn was analyzed in bulk samples and the toxicity potential (TP) was calculated for each metal. A canonical correspondence analysis (CCA) was applied to identify the relationship between environmental variables and metals concentrations. An average of 26.6 ± 10 mm of rainfall was obtained. A mean pH of 7.2 ± 0.3 and a mean electrical conductivity of 177.8 ± 8.7 µS cm−1 were observed. The average concentration of metals in all sites follows a descending order of Fe> Zn > Mn > Cu > Ni > Cd. The university site shows the highest averages of Fe, Zn, Cu, and Mn, which is attributed to its proximity to the metallurgical industry. The TP value of Cd reflects a risk in all sites and Fe only for the Universidad, Obispado, Pastora, and Santa Catarina sites, using as a reference value the Environmental Protection Agency (EPA) Drinking Water Regulations and Mexican norm NOM-127-SSA1-1994. The CCA analysis showed that only Ni and Cd had a strong correlation with the environmental variable of relative humidity of air. Full article
(This article belongs to the Special Issue Air Pollution in Urban and Industrial Areas)
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